Towards Circular Water Neighborhoods: Simulation-Based Decision Support for Integrated Decentralized Urban Water Systems

Article Towards Circular Water Neighborhoods: Simulation-Based Decision Support for Integrated Decentralized Urban Water Systems

Dimitrios Bouziotas 1,* , Diederik van Duuren 2,3, Henk-Jan van Alphen 1, Jos Frijns 1, Dionysios Nikolopoulos 4 and Christos Makropoulos 1,4

1 KWR Watercycle Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands; [email protected] (H.-J.v.A.); [email protected] (J.F.); [email protected] or [email protected] (C.M.) 2 Waterleiding Maatschappij Limburg (WML), Limburglaan 25, 6229 GA Maastricht, The Netherlands; [email protected] or [email protected] 3 International Centre for Integrated assessment and Sustainable development (ICIS), Maastricht University, Kapoenstraat 2, 6211 KR Maastricht, The Netherlands 4 Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens (NTUA), Iroon Politechniou 5, 157 80 Zografou, Athens, Greece; [email protected] * Correspondence: [email protected]; Tel.: +31-6-54294093

Received: 14 May 2019; Accepted: 9 June 2019; Published: 12 June 2019

Abstract: 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.

Keywords: circular water; decentralized systems; distributed solutions; greywater recycling; key performance indicators; rainwater harvesting; smart neighborhoods; urban water

1. Introduction The conventional, centralized model of urban water management that delivers ‘big pipes in’ from multiple water sources to urban areas and drives ‘big pipes out’ to dispose urban wastewater has generated (and will likely continue to deliver) significant benefits to cities. Through this ‘hard path’ of water, cities worldwide enjoy reliable, constant clean water supply, improved health services, efficient disposal of their wastewater and simple operational and management structures. However, this path also faces multiple challenges and comes at a high cost of ecological degradation,

Water 2019, 11, 1227; doi:10.3390/w11061227 www.mdpi.com/journal/water Water 2019, 11, 1227 2 of 25 social intrusion and capital intensity [1]. Challenges arise at the supply side, linked to aging—and progressively more susceptible to failures—large infrastructure, weak governance structures, limited investment opportunities for new infrastructure projects, but also a rapidly changing setting of water availability, driven by climate change and its impact on different water source types [2–4]. At the same time, the demand side also poses threats linked to rapid urbanization, demographic shifts and changing client behaviors that are often not met by adaptive policy changes [5–7]. Given this context, cities are becoming increasingly aware that there is a need to reconsider traditional water management models and substantially redesign the way freshwater resources are utilized [8]. A new, circular path of water is envisioned as an alternative to the conventional linear management model [1,9,10], which supplements large central infrastructure with an array of decentralized water options that are applied at different spatial scales within the city [11]. Water-aware appliances are envisioned to be used at a household level, in order to lessen demands at a household level, paired with neighborhood-scale decentralized measures, that aim to add local sources such as rainwater or groundwater to the distribution network, as well as recycle wastewater to cover part of the urban demands. This decentralized paradigm in water management aims at creating smart, water-aware units within the city, where multiple distributed technology options are integrated to provide a circular urban water management model that reduces-reuses-recycles, instead of the one that merely extracts, transports and disposes. The paradigm shift that is envisioned bears a close resemblance to broader socio-economic transitions that aim at reaching sustainability, such as the progression towards a model of circular economy [12,13]. This envisioned decentralization of urban water, however, will not come easily, as it brings a higher degree of system complexity and requires sectoral bodies, water utilities, communities and entrepreneurs to collaborate and co-design efficient implementation and uptake models [14,15]. Likewise, decision support systems that are designed based on the premise of central, externally provided water infrastructure now need to include the elaborate interplay between diverse decentralized technologies at multiple scales and across multiple urban water cycle domains, such as drinking water, wastewater and stormwater [16]. While numerous decision support frameworks of the metabolism type that target multiple urban water cycle streams exist, there are few applications in an integrated context that are able to include multiple decentralized options in a combined manner, let alone explore the underlying drivers of change such as future climates and social factors [17,18]. Studying the interplay between different technologies at different scales across multiple urban water cycle streams (drinking water, wastewater and stormwater), as well as finding a consistent way to translate this interplay into easily interpretable information for decision-making, remains a topic of ongoing research. The transition to new, circular urban water management models needs to be accompanied by holistic frameworks that are able to look into multiple water cycle domains, across many externalities (i.e., encompassing social, environmental, financial and technological factors) and can also quantify and visualize the advantages and disadvantages of decentralized systems in an efficient way. Aiming to contribute to this body of research, this study presents a simulation-based framework suitable for the design and evaluation of integrated decentralized systems at a neighborhood scale. The framework is able to model distributed technologies, deployed at different spatial scales, such as household measures (e.g., water-saving appliances) as well as more upscaled interventions (e.g., neighborhood-scale rainwater harvesting designs) and provides quantitative insight into different aspects of the urban water cycle, related to all water streams (drinking water (DW), wastewater (WW) and rainwater-runoff (RW)). The quantitative insight is provided by linking together 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 combined solutions at neighborhood scale with regards to water quantity and can be easily communicated to stakeholders and decision-makers. The framework is applied for an ex ante evaluation of SUPERLOCAL, a circular water neighborhood that is designed in the area of Limburg, in the Netherlands, in order to draw Water 2019, 11, 1227 3 of 25 conclusions on the performance of different integrated system designs that combine rainwater harvesting (RWH) with greywater recycling (GWR), among other circular water interventions.

2. Materials and Methods

2.1. A Simulation-Based Approach Towards (Re-)designing Circular Water Neighborhoods Evidently, introducing decentralized options at the city is not a trivial management task. Decentralized options can be introduced in a combined manner at multiple spatial scales [19], for instance when designing a new blue-green large recreational area [20] or at smaller spatial units, such as the neighborhood or household level. Regardless of scale, the introduction of multiple decentralized technologies in an integrated fashion leads to effects on multiple urban water cycle domains that are commonly not evaluated or managed together. There is reduction in the clean, freshwater requested from central, DW services. Local freshwater sources, such as rainwater, are captured and used locally, while the introduction of a RWH scheme has an effect on stormwater retention that leads to an altered (slower and lower) runoff response at the outlet. Part of the generated WW in the neighborhood is treated locally and reused, thus reducing the quantity of WW propagated downstream, e.g., to centralized sewer services. These multi-domain effects need to be part of an integrated performance assessment to allow for a multi-faceted evaluation of the system of interest to all stakeholders. Moreover, such an integrated framework needs ideally to reach beyond the urban water cycle and explore external driving factors, such as climate change, behavioral client shifts, demographic changes, capital and operational costs etc., in order to account for the strong uncertainties seen in urban water management [21]. Research questions therefore arise, asking for integrated frameworks able to assess the performance and impact of a combined decentralized system that: (a) takes into account the aforementioned effects on multiple urban water cycle domains, (b) that can be expanded to account for multiple dimensions (e.g., social, technological, environmental and financial), which can be then considered drivers of change in the system. In light of these questions, the contribution of this study is towards the development of a quantitative assessment framework, able to support decisions by evaluating the performance of the combined decentralized system in a way that can be easily interpreted by stakeholders. The demonstrated assessment framework links an urban water cycle model able to run predefined scenarios—which is the way to gain quantitative insights on the whole system performance—with a set of Key Performance Indicators (KPIs) that transform raw model output to scalar quantities that can be easily communicated to stakeholder groups. To apply this framework, the spatial unit of a neighborhood has been chosen, as it combines a wider array of applicable technologies and economy of scale over the household level [22], as well as a relatively small extent that allows replicability in multiple sites within a city or a set of similar cities. Within this context, the re-design of neighborhoods to include integrated decentralized systems can be defined as transitioning towards circular water neighborhoods, as an analogue to the transition towards circular cities [23]. The framework relies on urban water cycle model simulation [24] as a technique to mimic the response of the whole system ex ante and utilizes the Urban Water Optioneering Tool (UWOT) as a simulation testbed. UWOT is an urban water cycle model following the metabolism modelling type, able to simulate the complete urban water cycle by modelling individual water uses and technologies/options for managing them, starting from the household level and aggregating to a neighborhood or whole city scale [25,26]. UWOT simulates both urban water flows, i.e., potable water, wastewater and runoff, as well as their integration in terms of harvesting, reuse and recycling at different scales (from the household and neighborhood up to the city scale). UWOT has been developed and tested in previous urban water cycle modeling applications that include neighborhood-scale blue-green area design [20], whole city cycle modeling [26] and analyses of distributed neighborhood options under scenarios of urban growth [27], so it is in principle readily able to simulate integrated systems. This ability is demonstrated in a combined neighborhood-scale decentralized system in this study, building from experience in previous applications that have concentrated on one specific type of Water 2019,, 11,, 1227x FOR PEER REVIEW 44 of of 26 25

RWH or GWR but not a combined system [27] and a study of a simplified combined RWH and GWR systeminterventions at a single [20], household neighborhood-scale [28]. cases that feature either RWH or GWR but not a combined systemTo [27model] and urban a study water of a simplifiedflows, UWOT combined follows RWH the andso-called GWR system‘signal atlanguage’: a single household a signal-based [28]. systemsTo model analysis urban approach water flows, that starts UWOT from follows individual the so-called components ‘signal language’: (i.e., in-house a signal-based appliances, systems units thatanalysis use approachwater and that generate starts wastewater from individual or runoff components), and proceeds (i.e., in-house to the appliances,generation, unitstransmission, that use wateraggregation and generate and transformation wastewater or of runo waterff), anddemand proceeds signals to thethat generation, start from transmission,the household aggregation level and propagateand transformation towards the of water source demand of water signals demands, that start i.e., fromthe central the household drinkinglevel water and network propagate [29]. towards UWOT thenthe source proceeds of water to match demands, these i.e.,demands the central with drinkingsupply fr waterom different network source [29]. UWOTtypes [26] then and proceeds logs time to stepsmatch when these failure demands occurs, with i.e., supply the demand from di cannotfferent be source met by types provided [26] and suppl logsy. time This steps demand-oriented when failure conceptualization,occurs, i.e., the demand seen in cannot the right be met panel by providedof Figure supply.1, places This household demand-oriented and neighborhood conceptualization, demands asseen the in starting the right point panel of of every Figure study1, places and householdenables UWOT and neighborhood to simulate the demands whole asurban the startingwater system point tracingof every water study demands and enables from UWOT tap to to source simulate [26], the as wholeopposed urban to water water supply system flowing tracing waterfrom source demands to tapfrom (left tap panel to source of Figure [26], as1). opposed to water supply flowing from source to tap (left panel of Figure1).

Figure 1. The flows seen in real water systems (left) as well as in the UWOT demand-driven Figureconceptualization 1. The flows (right seen). in real water systems (left) as well as in the UWOT demand-driven conceptualization (right). 2.2. The Case Study: The Circular Water Neighborhood of SUPERLOCAL 2.2. The Case Study: The Circular Water Neighborhood of SUPERLOCAL The case study to demonstrate the framework is the project of SUPERLOCAL, a joint venture of four localThe case partners study with to demonstrate distinct roles the in the framework urban water is the cycle project (Table of1 SUPERLOCAL,), aiming at closing a joint the waterventure loop of fourin the local newly partners developed with area distinct of Parkstad roles in Limburgthe urban in water the Netherlands cycle (Table (Figure 1), aiming2). This at projectclosing comprises the water loopthe redesign in the newly of an urban developed district, area where of Parkstad one large Li flatmburg becomes in the renovated Netherlands and several (Figure housing 2). This units project are plannedcomprises to the bebuilt redesign on the of terrain an urban of three district, older, wh demolishedere one large flats. flat In orderbecomes to shift renovated from the and traditional, several housinglinear urban units water are planned management to be built model on tothe a terrain circular, of water-wise three older, alternative, demolished the flats. four In localorder partners to shift fromhave collaboratedthe traditional, to linear install, urba operaten water and management monitor a set model of decentralized to a circular, technologies water-wise that alternative, include: the four local partners have collaborated to install, operate and monitor a set of decentralized Installing water-saving household devices that have a reduced water footprint and lead to technologies• that include: lower demands at a tap (household) level. A portfolio of technologies such as vacuum toilets, • water-savingInstalling water-saving showers and household recirculation devices showers that have are planned a reduced to bewater installed footprint in di andfferent lead household to lower typesdemands (Table at 2a). tap (household) level. A portfolio of technologies such as vacuum toilets, water- Asaving rainwater showers harvesting and recirculation (RWH) scheme showers at are the pl neighborhoodanned to be installed scale that in collectsdifferent water household from • householdtypes (Table roofs 2). and public (impervious) areas, purifies it and uses it to cover drinking water (DW) • A rainwater harvesting (RWH) scheme at the neighborhood scale that collects water from household roofs and public (impervious) areas, purifies it and uses it to cover drinking water

Water 2019, 11, 1227 5 of 25

needs. Excess rainwater that is not captured is directed to a ‘water square’, a large infiltration basin designed to absorb rainwater through percolation. This measure constitutes a sustainable urban drainage system (SUDS) [30,31], aiming at capturing rainwater locally and not allowing it to reach the outlet of the neighborhood. A greywater recycling (GWR) scheme at the neighborhood scale that collects wastewater (WW) • from selected uses such as the shower and handbasin, purifies it through a nature-based helophyte filter and redirects it in the urban water cycle, for instance to be used in certain common uses such as a shared laundry unit and car wash facility. A secondary (black water BW) sewage system processing water from vacuum toilets and food • grinders in order to purify and reclaim resources and energy in a digester. This is an implementation of the ‘new sanitation’ concept, using the idea of separation of waste water at the source [32]. The inclusion of different sewage treatment options per (GW/BW) stream introduces a dual, parallel system that results in a higher GW quality and thus potential of reuse, as well as higher potential for energy and resource recovery in the BW stream rich in organic material. This concept requires the use of vacuum toilets (against other water-aware solutions), in order to maximize nutrient concentration in the BW stream and enable recovery.

Table 1. Main actors and responsibilities involved in the case study.

Organization Conventional Responsibilities Decentralized Responsibilities (SUPERLOCAL) Rainwater management (incl. capturing, transport and storage), public area development in regards Rainwater management (incl. capturing, Municipality rainwater harvesting, water square transport and discharge), (Kerkrade) and infiltration services sewage network (in cities) Operations: Vacuum station, sewage system (black and grey), and rainwater buffers Water extraction, production and distribution on Drinking water company Water extraction, production a local scale (WML) and distribution Operations: connection between rainwater buffers and drinking water production (optimization) Installation of household technologies, inhouse Social housing corporation Installation of household technologies piping and a second (vacuum) sewage system (HEEMwonen) and inhouse piping Operations: Common launderette, food grinders and car wash Sewage transport between Helophyte filter and buffers, and BW digestor Waste water company municipalities and WWTP, and waste Operations: pruning of helophyte filter, (WBL) water treatment and residues transport

These interventions are combined together in a set of proposed, integrated designs, in order to provide high-quality residential services that at the same time aim lead to: (a) less water use, which is the consequence of using appliances with a lower water footprint (i.e., water consumed per use) at a household level, (b) a slower runoff response, modified by the capacity of the system to store and absorb water and (c) an efficient water use policy at the local scale, where water remains locally for a longer time and is reused. This localization is based on employing neighborhood-scale harvesting, recycling and energy & material recovery technologies. As part of the proposed designs, two options are being considered as potential operational systems for SUPERLOCAL. In the first design option, greywater is treated locally and is then used to cover specific shared water demands, such as a common laundry and car wash facility, while rainwater is captured and treated locally to cover the rest of the DW demands. This is the most likely case to be implemented in SUPERLOCAL, with two distinct local treatment systems (RWH and GWR) targeting different water uses. The second option that is considered extends GW reusability by considering treated GW as having the same quality characteristics as rainwater following a natural filtering step, thus being able to be mixed together with RW in a shared buffer and thus target all domestic water uses. This option combines the two local treatment systems and is a more integrated experimental alternative that will be considered only if the treated GW quality of the project pilot is found to be Water 2019,Water 11, x 2019FOR, 11PEER, x FOR REVIEW PEER REVIEW 6 of6 26of 26

longer time and is reused. This localization is based on employing neighborhood-scale harvesting, longer time and is reused. This localization is based on employing neighborhood-scale harvesting, recycling and energy & material recovery technologies. recycling andAs energy part of & thematerial proposed recovery designs, technologies. two options are being considered as potential operational As partsystems of thefor SUPERLOCAL. proposed designs, In the firsttwo design options option, are greywater being considered is treated locally as potential and is then operational used to systems forcover SUPERLOCAL. specific shared In water the firstdemands, design such option, as a greywatercommon laundry is treated and locally car wash and facility, is then while used to cover specificrainwater shared is captured water and demands, treated locally such to as cover a common the rest of laundrythe DW demands. and car This wash is the facility, most likely while rainwatercase is captured to be implemented and treated in locallySUPERLOCAL, to cover with the resttwo ofdistinct the DW local demands. treatment This systems is the (RWH most and likely case to beGWR) implemented targeting different in SUPERLOCAL, water uses. The with second two op distincttion that localis considered treatment extends systems GW reusability (RWH and GWR) targetingby considering different treated water GW uses.as having The the second same qualityoption characteristics that is considered as rainwater extends following GW areusability natural filtering step, thus being able to be mixed together with RW in a shared buffer and thus target all by considering treated GW as having the same quality characteristics as rainwater following a natural domestic water uses. This option combines the two local treatment systems and is a more integrated Water 2019, 11, 1227 6 of 25 filtering step,experimental thus being alternative able to that be will mixed be considered together onlywith if RWthe treated in a shared GW quality buffer of theand project thus pilottarget is all domesticfound water to uses. be high This enough. option Both combines design options the two are local complemented treatment withsystems the same and useis a of more water-saving integrated experimentalhigh enough.appliances alternative Both on design a household that options will bescale, are considered complemented with the onlyexact ifmixture with the treated the ofsame appliances GW use quality of being water-saving of dependent the project appliances on pilot the is foundon a household to householdbe high scale,enough. type with(Table Both the 2). design exact mixture options ofare appliances complemented being with dependent the same on use the of household water-saving type appliances(Table2). on a household scale, with the exact mixture of appliances being dependent on the household type (Table 2).

Figure 2. Overview of the case study and household types of SUPERLOCAL.

Table 2. The case study and household types of SUPERLOCAL.

Experimental Unit Houses Apartments Total Houses FigureFigure 2. 2. OverviewOverview of of the the case case study study and and household household types types of of SUPERLOCAL. SUPERLOCAL. Abbreviation (a) (b) (c) (FigureTable 2) 2. The case study and household types of SUPERLOCAL. Table 2. The case study and household types of SUPERLOCAL. NumberUnit of dwellings Houses13 Experimental3 Houses Apartments113 129 Total Experimental WaterAbbreviation 2019Unit, 11Persons/dwelling, x FOR (Figure PEER2 REVIEW) Houses (a) (b)Apartments (c) 7 of 26 Total 2.2 (29) Houses1.8 (5) 2.0 (226) 260 Number of dwellings(persons) 13 3 113 129 Persons/dwelling (persons) 2.2 (29) 1.8 (5) 2.0 (226) 260 WaterAbbreviation 2019, 11, xType FOR PEER of toilet REVIEW Water Silent Standard 7 of 26 Type of shower (n) (a) Recirculation(b) (3) WaterSilent saving vacuum (113)(c) (113) 129 129 Type(Figure of toilet 2)(n) (n) savingSilentvacuum vacuum(13) (13) Standardvacuum vacuum (3) (3) Silent vacuum (113) 129 Water TypeTypeType of ofshower showerwashing (n) (n) Water saving (13)Recirculation Recirculation (3) 50% (3)Water regular saving (56); Water (113) 50% saving use (113)129 129 Number of dwellings Regularsaving13 (13) (13) Regular 3(3) 113 77 129 machine (n) shared launderette50% regular (5) (56); 50% use Type washing machine (n) Regular (13) Regular (3) 77 Persons/dwellingType washing 50% regular shared(56); 50% launderette use (5) Food grinder Regular2.2Water (29) (13) Regular1.8 (3) (5) Large shared water2.0 (226) saving 77 260 machine (n) Water saving (3) shared launderetteLarge shared (5) water saving25 (persons)(n) savingWater (13) saving (13) Water saving (3) on each level (9) 25 Food grinder (n) on each level (9) Food grinder Water Large shared water saving TypeTap(s) of toilet (n) RegularSilentRegular (39) (39)WaterRegular savingStandard (6) Regular (3) (6)Regular (226) Regular (226)27125 271 Tap(s)(n) (n) saving (13) on eachSilent level vacuum (9) (113) 129 (n) vacuum (13) vacuum (3) OutdoorOutdoor tap tap (n) (n) RegularRegular (13) (13)Regular (3) Regular (3)- (0) - (0)16 16 Tap(s) (n) Regular (39) Regular (6) Regular (226) 271 Waste Wastestreams: streams: = BW= sewage,BW sewage, = GW= GW sewage, sewage, == InIn soil soil Outdoor tap (n) Regular (13) Regular (3) - (0) 16

2.3.2.3. Formulating Formulating thetheWaste Performance streams: Assessment Assessment = BW Chainsewage, Chain for Decentralized for = DecentralizedGW sewage, Urban UrbanWater = In soilSystems Water Systems UsingUsing a a simulation simulation testbed testbed such such as asUWOT UWOT provides provides a detailed a detailed picture picture of the whole of the system whole system 2.3.response—in Formulating terms the Performance of requested Assessment DW, produced Chain for WW Decentralized and simulated Urban Waterrunoff—for Systems a given design response—in terms of requested DW, produced WW and simulated runoﬀ—for a given design option optionUsing that a encompasses simulation testbed all selected such astechnologies UWOT provides on a household a detailed and picture neighborhood of the whole scale. system This that encompasses all selected technologies on a household and neighborhood scale. This design response—indesign option terms could ofbe requested considered DW, a scenario produced in theWW neighborhood, and simulated encompassing runoff—for assumptionsa given design on optionoptionthe mixture, could that beencompasses usage considered and design all a scenarioselected attributes technologies in of the technologies, neighborhood, on a householdbut also encompassing implicitly and neighborhood including assumptions assumptions scale. on This the mixture, usagedesignon climate and option design (through could attributes thebe useconsidered of ofrainfall technologies, a scenariodata to generate in butthe neighborhood, alsorunoff) implicitly and client encompassing including behavior (by assumptionsassumptions assuming a onset on climate theoccupancy mixture, and usage frequency and design of use).attributes Likewise, of technologies, a neighborhood but also that implicitly features including the same assumptions spatial and onoccupancy climate (through characteristics the use but of rainfallno water-saving data to generate or decentralized runoff) and measures, client behavior i.e., a unit (by assumingof linear water a set occupancymanagement and that frequency requests ofDW use). from Likewise, the central a neighborhoodnetworks and quicklythat features disposes the WWsame and spatial runoff and to occupancycorresponding characteristics central services, but no could water-saving be considered or de anothercentralized scenario measures, able to i.e., be modeleda unit of withlinear UWOT. water managementIt becomes thus that evident requests that DW UWOT from isthe able central to model networks different and ‘realms’quickly disposesin the form WW of andscenarios runoff that to correspondingrepresent management central services, choices, could climate be considered characteristics another and scenario social ablebehaviors to be modeled through with its required UWOT. Itinputs becomes and thuscomponent evident topology that UWOT (i.e., is house able to types, model RWH different and/or ‘realms’ GWR treatmentin the form unit). of scenarios A basis forthat a representsimulation-based management assessment choices, framework climate couldcharacteristics then start and with social the following behaviors conceptualization: through its required “One inputscould assessand component the performance topology of (i.e.,a decentralized house types, sy RWHstem and/orby comparing GWR treatment the ‘decentralized’ unit). A basis scenario for a simulation-basedwith the current assessment reality, i.e., framework baseline couldscenario, then that start reflects with the linear following management conceptualization: and relies “One on couldcentralized assess networks.” the performance of a decentralized system by comparing the ‘decentralized’ scenario with Thisthe conceptualization,current reality, i.e., however, baseline has scenario, a caveat: thatperformance reflects linearassessment management is directly and dependent relies onon centralizedthe output networks.”of the model, which generates a wealth of data on daily time steps. There needs to be a higherThis level conceptualization, of abstraction from however, (complex) has a datacaveat: prod peucedrformance by the assessment model to the is directly (lean) data dependent needed on to theevaluate output performance of the model, and which reach generates decisions. a This wealth abstraction of data oncan daily be reached time steps. by introducing There needs a numberto be a higherof key levelperformance of abstraction indicators from (complex)(KPIs), which data reprprodesentuced characteristicby the model performanceto the (lean) datametrics needed of the to evaluatesystem. performance and reach decisions. This abstraction can be reached by introducing a number of keyThe performance use of KPIs toindicators convey information (KPIs), which is considered represent important,characteristic as they performance can be viewed metrics as a crucialof the system.link between assessment frameworks and the decision-making process, since they distil the—often complex—informationThe use of KPIs to convey of assessment information methodologies is considered such important, as modeling-based as they can frameworksbe viewed as in a crucialsimple linkquantities, between able assessment to be interpreted frameworks efficiently and the and decision quickly-making by non-experts process, sincesuch theyas management distil the—often units complex—informationand stakeholders [33]. Identifyingof assessment efficient methodologies KPIs for eachsuch decision-makingas modeling-based domain frameworks is no trivial in simple task quantities,and is the subjectable to ofbe extensive interpreted research efficiently in multiple and quickly fields by[34,35], non-experts including such urban as management water [36–39]. units The andnon-trivial stakeholders nature [33]. of forming Identifying KPIs efficient comes from KPIs the for interaction each decision-making of multiple factorsdomain that is no affect trivial design, task andsuch is as: the (a) subject the project of extensive aim and research stakeholder in multiple target groups,fields [34,35], (b) the including assessment urban framework water [36–39]. and model The non-trivialcapabilities, nature as the ofKPIs forming need toKPIs be derived comes from from thethe interactiondata output of generated multiple by factors the model, that affect (c) the design, needs suchof decision-makers, as: (a) the project as aimthe KPIsand stakeholderneed to include target mu groups,ltiple system (b) the aspects assessment that affectframework decision-making, and model capabilities,be understandable as the KPIs and, need to the to begreatest derived possible from th exe datatent, output co-designed generated with by the the interested model, (c) end-users. the needs ofWith decision-makers, regards to this as consideration, the KPIs need theto include simulati muon-basedltiple system assessment aspects framework that affect conceptualizationdecision-making, becan understandable be rephrased and,as follows: to the greatest“One may possible assess ex thetent, performance co-designed withof a thedecentralized interested end-users.system by Withcomparing regards the to ‘decentralized’ this consideration, scenario the withsimulati the on-basedbaseline scenario assessment (current framework reality) conceptualizationthat reflects linear canmanagement be rephrased and reliesas follows: on centrali “Onezed may technologies assess the via performancea number of Keyof a Performance decentralized Indicators.” system by comparing the ‘decentralized’ scenario with the baseline scenario (current reality) that reflects linear management and relies on centralized technologies via a number of Key Performance Indicators.”

Water 2019, 11, 1227 7 of 25

(through the use of rainfall data to generate runoff) and client behavior (by assuming a set occupancy and frequency of use). Likewise, a neighborhood that features the same spatial and occupancy characteristics but no water-saving or decentralized measures, i.e., a unit of linear water management that requests DW from the central networks and quickly disposes WW and runoff to corresponding central services, could be considered another scenario able to be modeled with UWOT. It becomes thus evident that UWOT is able to model different ‘realms’ in the form of scenarios that represent management choices, climate characteristics and social behaviors through its required inputs and component topology (i.e., house types, RWH and/or GWR treatment unit). A basis for a simulation-based assessment framework could then start with the following conceptualization: “One could assess the performance of a decentralized system by comparing the ‘decentralized’ scenario with the current reality, i.e., baseline scenario, that reflects linear management and relies on centralized networks.” This conceptualization, however, has a caveat: performance assessment is directly dependent on the output of the model, which generates a wealth of data on daily time steps. There needs to be a higher level of abstraction from (complex) data produced by the model to the (lean) data needed to evaluate performance and reach decisions. This abstraction can be reached by introducing a number of key performance indicators (KPIs), which represent characteristic performance metrics of the system. The use of KPIs to convey information is considered important, as they can be viewed as a crucial link between assessment frameworks and the decision-making process, since they distil the—often complex—information of assessment methodologies such as modeling-based frameworks in simple quantities, able to be interpreted efficiently and quickly by non-experts such as management units and stakeholders [33]. Identifying efficient KPIs for each decision-making domain is no trivial task and is the subject of extensive research in multiple fields [34,35], including urban water [36–39]. The non-trivial nature of forming KPIs comes from the interaction of multiple factors that affect design, such as: (a) the project aim and stakeholder target groups, (b) the assessment framework and model capabilities, as the KPIs need to be derived from the data output generated by the model, (c) the needs of decision-makers, as the KPIs need to include multiple system aspects that affect decision-making, be understandable and, to the greatest possible extent, co-designed with the interested end-users. With regards to this consideration, the simulation-based assessment framework conceptualization can be rephrased as follows: “One may assess the performance of a decentralized system by comparing the ‘decentralized’ scenario with the baseline scenario (current reality) that reflects linear management and relies on centralized technologies via a number of Key Performance Indicators.” Accounting for the aforementioned remarks, the simulation-based assessment framework developed in this study is seen in Figure3, which depicts it in a form of an assessment chain, i.e., a sequence of steps that link scenario development (i.e., the formulation or alternative modeling ‘realities’), model simulation and end-user performance evaluation assisted by the formulation of suitable KPIs. The framework comprises the following steps, also mapped in Figure3:

1. Deﬁne the scenario (i.e., modeling reality) by deciding on climate, technological and social dimensions that aﬀect model inputs.

2. Convert this modeling reality to model input that comes in the form of n scalars or time-series Xi that deﬁne the n-dimensional input space →X:

→ X = X , X , ... , Xn (1) { 1 2 } In the case of UWOT and as aforementioned, assumptions on climate affect the rainfall time-series needed to calculate runoff, while assumptions on technology affect the choice of interventions at a neighborhood scale (household appliances, RWH, GWR). 3. Define the design attributes of the given scenario. These attributes reflect design and operational characteristics for a given technological reality, such as the treatment capacity and buffer storage Water 2019, 11, x FOR PEER REVIEW 8 of 26

Accounting for the aforementioned remarks, the simulation-based assessment framework developed in this study is seen in Figure 3, which depicts it in a form of an assessment chain, i.e., a sequence of steps that link scenario development (i.e., the formulation or alternative modeling ‘realities’), model simulation and end-user performance evaluation assisted by the formulation of suitable KPIs. The framework comprises the following steps, also mapped in Figure 3: 1. Define the scenario (i.e., modeling reality) by deciding on climate, technological and social dimensions that affect model inputs. 2. Convert this modeling reality to model input that comes in the form of n scalars or time-series

𝑋 that define the n-dimensional input space 𝑋⃗:

𝑋⃗ ={𝑋,𝑋,…,𝑋} (1) Water 2019, 11, 1227 8 of 25 In the case of UWOT and as aforementioned, assumptions on climate affect the rainfall time- series needed to calculate runoff, while assumptions on technology affect the choice of interventionsof a RWH/GWR at a designneighborhood option, scale and come(household in the appliances, form of an RWH, m-dimensional GWR). model parameter 3. Definespace → µthewith design m parameters attributes ofµ ithe: given scenario. These attributes reflect design and operational characteristics for a given technological→µ reality,= µ such, µ , ... as ,theµm treatment capacity and buffer storage(2) { 1 2 } of a RWH/GWR design option, and come in the form of an m-dimensional model parameter → → 4. spacePerform 𝜇⃗ with model m simulationparameters using𝜇: the input space X and parameter space µ, in order to extract → the system response in the form of time-series Yi in the k-dimensional output space Y: 𝜇⃗ ={𝜇,𝜇,…,𝜇} (2)

→ 4. Perform model simulation using the inputY = spaceY1, Y 2𝑋⃗, ... and, Y kparameter space 𝜇⃗, in order to extract(3) { } the system response in the form of time-series 𝑌 in the k-dimensional output space 𝑌⃗: In the case of UWOT, the output space includes water cycle streams that come as time-series of

demands (i.e., requested DW), produced𝑌⃗ ={𝑌,𝑌 WW,,…,𝑌 simulated} runoﬀ etc. (3) 5. Reduce the complexity of output data by transforming model outputs to KPIs. This step can be In the case of UWOT, the output space includes water cycle streams that come as time-series of → → demandsalso viewed (i.e., as requested a statistical DW), transformation produced WW, process simulatedf (Y) runoffof the etc. output time-series Y to scalars 5. Reducethat represent the complexity system performance.of output data by Since transf KPIsorming aim tomodel compare outputs baseline to KPIs. with This decentralized step can be ⃗ → → alsosystem viewed performance, as a statistical they can transformation be considered process a statistical 𝑓(𝑌) transformation of the output processtime-seriesf (Y a𝑌,⃗ toYb scalars) across that represent system→ performance.→ Since KPIs aim to compare baseline with decentralized the output vectors Ya and Yb of two scenarios (a) and (b), where (a) is typically the baseline ⃗ ⃗ systemscenario performance, and (b) is the they decentralized can be consider scenario.ed a statistical transformation process 𝑓𝑌, 𝑌 ⃗ ⃗ 6. acrossUse the the calculated output vectors KPIs to evaluate𝑌 and 𝑌 the of performance two scenarios of the(a) system.and (b), where (a) is typically the 7. baselineIn case thescenario performance and (b) is notthe decentralized deemed adequate, scenario. two feedback options are possible—to return

6. Useback the to calculated step (3) and KPIs try anotherto evaluate design the attributeperformance (in order of the to system. increase the eﬃciency of the deﬁned

7. Inproposed case the decentralized performance system),is not deemed or change adequate the mixture, two feedback of interventions options altogether are possible—to and try anotherreturn backscenario, to step thus (3) goingand try back another to step design (1). attribute (in order to increase the efficiency of the defined proposed decentralized system), or change the mixture of interventions altogether and try another scenario, thus going back to step (1).

Figure 3. The developed simulation-based framework in the form of an assessment chain. Figure 3. The developed simulation-based framework in the form of an assessment chain. 2.4. Definition of KPIs for Decentralized Urban Water Systems 2.4. Definition of KPIs for Decentralized Urban Water Systems Evidently, the fifth step in the assessment chain is based the definition of suitable KPIs which, as aforementioned, have to take into account the decision-maker needs as well as modeling limitations.

Following communication with the project partners for SUPERLOCAL, five quantitative KPIs are defined in this study that relate to all major urban water cycle streams (DW, WW and runoff). The KPIs are formed from the comparison of the centralized, business-as-usual scenario against the decentralized scenario and are normalized to a common orientation in [0,1], indicating 0 for poor performance (i.e., no improvement) and 1 for ideal performance and perfect improvement compared to the centralized system. This normalization allows them to be dimensionless, directly comparable and able to be visualized together in the form of radar charts [40]. The five KPIs developed for decentralized systems as part of this study are: Water 2019, 11, 1227 9 of 25

The achieved reduction in household consumption RHC (or otherwise % household demand • reduction): Dh Dh RHC = decentral − central (4) h Dcentral with Dh denoting household DW demands averaged over time. This is DW KPI that can be viewed as a tap-level water demand management metric, describing the reduction in water requested at the household level before any upscaled measures such as RWH and GWR operate. The reduction at this household scale is caused by water-saving appliances, which reduce the water usage per appliance d. The achieved reduction in clean DW (RDW) requested from the external, central service • (or otherwise % reduction in the demand requested from central water utility):

Dn Dn decentral − central RDW = n (5) Dcentral

with Dn denoting neighborhood DW demands averaged over time. This is a DW KPI that can be viewed as a more representative measure of the system autonomy, dependent on all decentralized technologies in place. Speaking the ‘signal language’ of UWOT, it represents the demand signal that cannot be covered by local (harvested or reused) DW supply and has to be met by the central utility through the connection to the mains. The achieved reduction in WW (RWW) that leaves the system (or otherwise % reduction of WW • pushed to outlet):

QWW QWW = decentral − central RWW WW (6) Qcentral with QWW denoting the volume of wastewater that leaves the neighborhood averaged over time. This is a WW KPI that constitutes another measure of neighborhood autonomy, now regarding the produced WW that cannot be stored or treated locally and is propagated externally (e.g., through the centralized sewer network). The achieved runoﬀ reduction RAR (or otherwise % reduction of runoﬀ): •

runo f f runo f f Qdecentral Qcentral RAR = − (7) runo f f Qcentral

where Qruno f f is the volume of runoff leaving the neighborhood, averaged annually or over the entire simulation time. This is a KPI related to runoff that measures the efficiency of the decentralized system to retain water in the long term. The achieved flood event reduction RER (or otherwise average % reduction of peak event runoff): •

 event runo f f event runo f f   Q Q   decentral − central  RER = E  (8)  event runo f f  Qcentral

which is the mean value of the runoff reduction observed at the event level, with Qevent runo f f denoting the peak runoff observed during a simulated flood event. This is a runoff-related KPI that is measured at a smaller timescale (i.e., on a per-event basis) and depicts the efficiency of the decentralized system in mitigating floods. Water 2019, 11, 1227 10 of 25 Water 2019, 11, x FOR PEER REVIEW 10 of 26 WaterWater 20192019,, 1111,, xx FORFOR PEERPEER REVIEWREVIEW 1010 ofof 2626 Water 2019, 11, x FOR PEER REVIEW 10 of 26 • Water•• The 2019The reliability, 11reliability, x FOR PEER ofof REVIEW decentralizeddecentralized system system design design REL, REL, which which is defined is defined in a insimulation-based a simulation-based10 of 26 • • TheThe reliabilityreliability ofof decentralizeddecentralized systemsystem designdesign REL,REL, whichwhich isis defineddefined inin aa simulation-basedsimulation-based • framework as the % of time steps that the system operated well. Reliability is a probabilistic frameworkframeworkTheframework reliability as asas thethetheof decentralized %%% ofof time time stepssteps steps system thatthat that thethedesign the systsyst system ememREL, operatedoperated which operated iswell.well. defined well. ReliabiReliabi Reliabilityinlitylity a simulation-basedisis aa probabilisticprobabilistic is a probabilistic • Theconcept reliability with strong of decentralized links to simulation system modeling design REL, [41–43] which generally is defined defined in aas: simulation-based conceptconceptframeworkconcept with withwith as strong strongstrong the % linkslinks of time toto simulationsimulation simulationsteps that modelingmodelingthe modeling system [41–43][41–43] operated [41– 43 generallygenerally] generallywell. Reliabidefineddefined definedlity as:as: is as:a probabilistic framework as the % of time steps that the system operated well. Reliability is a probabilistic concept with strong links to simulation modeling [41–43] generally defined as: REL = 1𝑃𝑌=1 (9) REL = 1𝑃𝑌=1=1 (9)(9) concept with strong links to simulationREL = 1𝑃𝑌 modeling=1 [41–43] generally nY f defined as: (9) RELREL = 1𝑃𝑌= 1 =1P Y f = 1 (9) (9) with 𝑃𝑌 being the probability of either system failure or inefficiency. It is computed as the with 𝑃𝑌𝑃𝑌 being being thethe probabilityprobabilityREL ofof = eithereither1𝑃𝑌 systemsystem−=1 failurefailure oror − inefficiency.ninefficiency.total ItIt isis computedcomputed asas thethe with being the probability of either system failure or inefficiency. It is computed as(9) the withrelative 𝑃𝑌 frequency being the of probabilitytime steps ofwith either failure system or inefficiencyfailure or inefficiency. 𝑛 /𝑛 Itand is computedwith 𝑌 being as the a relativerelative frequencyfrequency ofof timetime stepssteps withwith failurefailure oror inefficiencyinefficiency 𝑛/𝑛/𝑛 andand withwith 𝑌 beingbeing aa relative frequency of time steps with failure or inefficiency 𝑛 /𝑛 and with 𝑌 being a withrelative 𝑃𝑌 frequency being the of probabilitytime steps ofwith either failure system𝑌 ⃗or inefficiencyfailure or inefficiency. 𝑛 /𝑛 Itand is computedwith 𝑌 being as the a withlogicallogicalP Y conditionconditionf being the targetingtargeting probability thethe outputoutput of either spacespace system 𝑌⃗⃗ thatthat failure isis usedused or ine totoffi distinguishdistinguishciency. Itis failedfailed computed oror inefficientinefficient as the relative relativelogical conditionfrequency targeting of time stepsthe output with failurespace 𝑌or that inefficiency is used to 𝑛 distinguish/𝑛 and failed with or 𝑌 inefficient being a frequencytime states of from time normal steps with operational failure orconditions inefficiency𝑌⃗ duringn simulation./n and In with thisY study,being the a focus logical is conditionon timelogicaltime statesstates condition fromfrom normal normaltargeting operationaloperational the output conditionsconditions space duringduringthat isY fsimulation.usedsimulation.total to distinguish InIn thisthis study,study, ffailed thethe or focusfocusinefficient isis onon logicalthe operational condition inefficiency targeting the for outputstorage space treatmen 𝑌⃗ thatt systems, is used i.e.,to distinguishmeasured asfailed the countor inefficient of time thetimethe operationaloperational states from inefficiency inefficiencynormal operational→ forfor storagestorage conditions treatmentreatmen duringtt systems,systems, simulation. i.e.,i.e., measuredmeasured In this study, asas thethe the countcount focus ofof istimetime on targetingtimesteps states during the from output simulation normal space whenoperationalY that the isstorage usedconditions tocapacity distinguish during of these simulation. failed systems orine isIn full, ffithiscient thestudy, treatment time the states focus capacity from is onnormal stepsthesteps operational duringduring simulationsimulation inefficiency whenwhen for thethe storage storagestorage treatmen capacitycapacityt ofofsystems, thesethese systemssystems i.e., measured isis full,full, thethe as treatment treatmentthe count capacity capacityof time operationaltheis inadequate operational conditions and inefficiency the incoming during for simulation. storageuntreated treatmen water In this overflowst systems, study, the fromi.e., focus measured the istreatment on theas the operational unit. count of inetimeffi ciency stepsisis inadequateinadequate during simulation andand thethe incomingincoming when the untreateduntreated storage capacity waterwater overflowsoverflows of these systems fromfrom thethe is treatmenttreatmentfull, the treatment unit.unit. capacity forsteps storage during treatment simulation systems, when the i.e., storage measured capacity as of the these count systems of time is full, steps the duringtreatment simulation capacity when isThe inadequate defined KPIs and theare incomingsummarized untreated in Table water 3. Unlike overflows the firstfrom five the KPIstreatment that focusunit. on volume theisTheThe storageinadequate defineddefined capacity KPIs KPIsand theareare of incoming summarized thesesummarized systems untreated inin is TableTable full, water 3. the3. UnlikeUnlike overflows treatment thethe firstfirstfrom capacity fivefive the KPIstreatmentKPIs is inadequate thatthat focusunit.focus andonon volumevolume the incoming reductionThe defined (Equation KPIs (4) are to summarized (8)), the reliability in Table of 3. decentralized Unlike the first system five KPIsdesign that (Equation focus on (9))volume is a reductionreductionuntreated (Equation(Equation water overflows(4)(4) toto (8)),(8)), fromthethe reliabilityreliability the treatment ofof decentralizeddecentralized unit. systemsystem designdesign (Equation(Equation (9))(9)) isis aa measureThe defineddescribing KPIs how are reliablesummarized different in Table elements 3. Unlike of a specificthe first desfiveign KPIs option that are.focus It onthus volume helps measurereductionmeasure describing describing(Equation howhow(4) to reliablereliable (8)), the didi fferentfferentreliability elementselements of decentralized ofof aa specificspecific system desdesignign design optionoption (Equation are.are. ItIt thusthus (9)) helpshelps is a reductionevaluate whether (Equation the selected(4) to (8)), design the attributesreliability ofof th decentralizede integrated decentralized system design system, (Equation i.e., the (9)) storage is a measureevaluateevaluateThe defined whetherwhetherdescribing KPIs thethe how selectedselected are reliable summarized designdesign di fferentattributesattributes in elements Table ofof thth3ee . integratedintegrated of Unlike a specific the decentralizeddecentralized firstdesign five option KPIs system,system, are. that i.e.,i.e., It focus thethusthe storagestorage helps on volume measurevolume of describing a buffer, treatment how reliable capacity different of a unit elements etc., are of well-selected a specific des andign suitable option toare. the It hydrological thus helps reductionvolumeevaluatevolume ofof (Equationswhether aa buffer,buffer, the treatmenttreatment (4) selected to (8)), capacitydesigncapacity the reliability attributes ofof aa unitunit of of etc.etc. decentralizedth,, e areare integrated well-selectedwell-selected decentralized system andand design suitablesuitable system, (Equation toto thethe i.e., hydrologicalhydrological the (9)) storage is a measure evaluateregime of whether the area, the i.e., selected the simulated design attributes runoff. Itof ththuse integrated provides decentralizedguidance to potential system, i.e.,changes the storage in the describingvolumeregimeregime ofof how theathe buffer, area,area, reliable treatmenti.e.,i.e., di thethefferent simulatedsimulated capacity elements ofrunoff.runoff. a unit of a ItIt etc. specific thth,usus are providesprovides well-selected design guidance optionguidance and are. suitabletoto Itpotentialpotential thus to helps the changeschanges hydrological evaluate inin thethe whether volumedesign (theof a feedbackbuffer, treatment loop back capacity to step of (3) a unit described etc., are in well-selected Figure 3) and and can suitable be applied to the in hydrological elements of regimedesigndesign (the(theof the feedbackfeedback area, i.e., looploop the backback simulated toto stepstep runoff. (3)(3) describeddescribed It thus inprovidesin FigureFigure guidance3)3) andand cancan to bebe potential appliedapplied changesinin elementselements in the ofof theregimethe selected integrated of the design area, system attributes i.e., that the feature simulated of the a integratedstorage runoff. and/or It decentralizedthus treatment provides unit, system,guidance such i.e., asto the thepotential RWH, storage changesGWR volume or inSUDS ofthe a buffer, thedesignthe integratedintegrated (the feedback systemsystem loop thatthat featurebackfeature to aastep storagestorage (3) described and/orand/or treatmenttreatment in Figure unit,unit, 3) and suchsuch can asas bethethe applied RWH,RWH, GWRGWRin elements oror SUDSSUDS of treatmentdesignunit in (theSUPERLOCAL. capacity feedback of aloop unit back etc., to are step well-selected (3) described and in Figure suitable 3) and to the can hydrological be applied in regimeelements of of the area, theunitunit integrated inin SUPERLOCAL.SUPERLOCAL. system that feature a storage and/or treatment unit, such as the RWH, GWR or SUDS i.e.,theunit the integrated in simulated SUPERLOCAL. system runo that ff. Itfeature thus providesa storage and/or guidance treatment to potential unit, such changes as the inRWH, the designGWR or (the SUDS feedback loopunit back in SUPERLOCAL. to step (3) described Table in3. The Figure KPIs3 defined) and can as part be of applied the framework. in elements of the integrated system TableTable 3.3. TheThe KPIsKPIs defineddefined asas partpart ofof thethe framework.framework. that feature a storage and/orTable treatment 3. The KPIs unit, defined such as as part the of RWH, the framework. GWR or SUDS unit in SUPERLOCAL. KPI Unit Description Stream KPIKPI Table 3. The KPIsUnitUnit defined as part of theDescription Descriptionframework. StreamStream

KPI Table 3. TheUnit KPIs defined as part ofDescription the framework. Stream Tap-level WDM metric, AchievedKPI reduction Unit Tap-levelDescription WDM metric, Stream Achieved reduction % of demand reductionreductionTap-level inin WDMwaterwater requestedrequestedmetric, DW— 1 Achievedinin householdhousehold reductionKPI % of demand Unit reduction in Descriptionwater requested DW— Stream 1 in household % ofreducedreduced demand reductionTap-levelfromfrom householdshouseholds in WDMwater requestedmetric, beforebefore demandsDW— 1 consumptionAchievedconsumptionin household reduction (RHC)(RHC) reduced fromTap-level households WDM metric, before reduction demands consumptionAchieved reduction (RHC) in % ofreduced demand reductionfromRWH/GWR households in water take requested beforeplace demandsDW— 1 consumptionin household (RHC) % of demand RWH/GWRin water requestedtake place from household consumption reduced fromRWH/GWR households take beforeplace demandsDW—demands consumptionReduction in (RHC)(clean) % of reductionreduced of householdsMeasure of before system RWH /GWR ReductionReduction in(RHC)in (clean)(clean) %% ofof reductionreduction ofof RWH/GWRMeasureMeasure ofof take systemsystem place 1 water requested from demand autonomy, dependenttake place on al DW— 2 waterwaterReduction requestedrequested in (clean) fromfrom % of demanddemandreduction of autonomyautonomyMeasure,, dependentdependent of system onon alal DW—DW— 22 Reductioncentral servicein (clean) %requested of reduction from of techsMeasure in place of (appliances, system demands waterReductioncentralcentral requested serviceservice in (clean) from waterrequestedrequesteddemand% of reduction fromfrom of autonomytechstechsMeasure inin placeplace, dependent of system(appliances,(appliances, autonomy on al , demandsdemandsDW— 2 water requested(RDW) from centraldemand service autonomyRWH,, dependent GWR) on al DW— 2 centralrequested(RDW)(RDW) service from central requestedcentralcentraldemand serviceservice from requested techsdependent inRWH,RWH, place GWR) onGWR)(appliances, al techs in place demandsDW—demands central(RDW)service service (RDW) requestedcentralfrom centralservice from service techs (appliances,inRWH, place GWR) (appliances, RWH, GWR) demands 2 Measure of system (RDW) central service MeasureRWH, ofGWR) systemsystem Reduction in WW Measure of system Reduction in WW % of WW autonomyMeasure or of (vice system versa) Generated 3 that leaves the %% ofof WWWW autonomyautonomyMeasure of ororsystem (vice(vice versa) autonomyversa) orGeneratedGenerated 33 ReductionthatthatReduction leavesleaves in in thetheWW WW that Measure of system %%reducedreduced of of WW reduced autonomy(vicedependence versa) or dependence (viceon central versa) on centralGeneratedWWGenerated WW 3 Reductionleavessystemsystemthat leaves the (RWW)(RWW) in system theWW (RWW) reduced dependence on central WW system (RWW) %reduced of WW autonomyservicesservicesdependenceservices (sewer(sewer or (sewer (viceon network)network) central versa) network) GeneratedWW 33 systemthat leaves (RWW) the services (sewer network) reduced servicesdependence (sewer on network) central WW system (RWW) Measure of the servicesMeasure (sewer of network) the Achieved runoff % of runoff Measure of the 4 Achieved runo runoffff reduction % of% runoff of runo ff SUPERLOCALMeasureMeasure of the of ability SUPERLOCALthe to Runoff 4 reductionAchievedreduction runoff(RAR)(RAR) reducedreduced% of runoff (annual)(annual) SUPERLOCAL ability to RunoffRuno ff 4 reduction(RAR) (RAR) reducedreduced (annual) (annual) SUPERLOCALMeasureholdability water to of holdability the water to Runoff Achievedreduction runoff(RAR) reduced% of runoff (annual) hold water 44 SUPERLOCALhold water ability to Runoff reduction (RAR) reduced (annual) Measure of the Achieved flood event % of event-based Measurehold water of the 5 Achieved flood event % of event-based SUPERLOCALMeasure of ability the to Runoff 5 AchievedreductionreductionAchieved flood (RER)(RER) flood event event %runoffrunoff of %event-based of reducedreduced event-based SUPERLOCALMeasure of the ability SUPERLOCAL to Runoff 5 reduction (RER) runoff reduced SUPERLOCALmitigateMeasure flood of ability thepeaks to RunoffRuno ff Achievedreductionreduction flood (RER) event (RER) %runoff of event-basedruno reducedff reduced mitigateability to mitigateflood peaks flood peaks 5 SUPERLOCALmitigate flood ability peaks to Runoff 5 reduction (RER) runoff reduced How reliable different parts % of time steps HowHowmitigate reliablereliable flood differentdifferent peaks partsparts %% ofof timetime stepssteps System design % of time steps thatHowof Howthe reliable systemreliable different arediff againsterent parts parts of 6 System design design reliability that%that of thethetime systemsystem steps of the system are against Runoff 6 reliabilitySystem design (REL) that thethe system system Howofinefficiencythe the reliable system system are different(storage are against against full, inepartsffi ciency RunoffRuno ff reliabilityreliability(REL) (REL)(REL) %operated of time stepswell inefficiencyinefficiency (storage(storage full,full, 6 System design thatoperatedoperated theoperated system wellwell well of the (storagesystemoverflow) full,are against overflow) Runoff 66 reliability (REL) that the system inefficiencyoverflow)overflow) (storage full, Runoff reliability (REL) operated well inefficiency (storage full, operated well overflow) overflow)

Water 2019, 11, 1227 11 of 25

Water 2019, 11, x FOR PEER REVIEW 11 of 26

3. Analysis3. Analysis and Resultsand Results 3.1. Bringing SUPERLOCAL to the UWOT Modeling Domain 3.1. Bringing SUPERLOCAL to the UWOT Modeling Domain In orderIn order to model to model a casea case at at a neighborhood neighborhood scale scale at its at native its native daily dailysimulation simulation time step, time UWOT step, UWOTrequires requires data data on the on the‘tap’ ‘tap’ side, side, as well as wellas info asrmation information on the on design the designattributes attributes of either of option. either option.Required Required data data on the on ‘tap’ the ‘tap’ side sideinclude include the house the house types typesof the ofneighborhood, the neighborhood, water usage water d usage(e.g., ind (e.g., inL/use/day) L/use/day) of of the the applianc applianceses installed installed in in each each household household and and the the occupancy occupancy occocct tofof each each household. household. This datasetThis dataset is provided is provided by the by project the project venture venture and can and be can seen be in seen Appendix in AppendixA, as a supplementA, as a supplement to Table to2. UWOTTable then 2. models UWOT the then demand models requests the demand for clean requests water for at clean an appliance water at levelan appliance as Q level= das 𝑄f,t occ=t, dem, t × × where𝑑𝑓ft is the𝑜𝑐𝑐 daily , where frequency ft is the of daily use of frequency each appliance. of use of Appliance each appliance. demands Appliance can then demands be aggregated, can then throughbe aggregated, corresponding through multipliers, corresponding to the multipliers, neighborhood to the scale neighborhood based on the scale number based on of householdsthe number for eachof households type. Frequency for each data type. is Frequency also provided data byis al theso provided venture partnersby the venture based partners on past based observations, on past seen inobservations, AppendixA seen. UWOT in Appendix further A. requires UWOT core further design requires attributes core design of RWH attributes and GWR of RWH systems, and GWR such 3 as thesystems, average dailysuch treatmentas the average capacity daily (e.g., treatment in m3/day) capacity and design (e.g., in storage m /day) for and each design buffer storage unit (e.g., for in each m3). buffer unit (e.g., in m3). These attributes are seen in Table 4. Finally, UWOT includes a rainfall-runoff These attributes are seen in Table4. Finally,UWOT includes a rainfall-runoff module to simulate generated module to simulate generated stormwater based on point rainfall data [20], as well as information on stormwater based on point rainfall data [20], as well as information on the soil infiltration capacity of the soil infiltration capacity of pervious areas. Daily rainfall time-series from a gauge in proximity to pervious areas. Daily rainfall time-series from a gauge in proximity to SUPERLOCAL are provided SUPERLOCAL are provided (KNMI precipitation station Roermond), covering a range of 31 years (KNMI(1986–2016) precipitation and station comprising Roermond), a total covering of 11,324 a rangetime steps, of 31 yearsalong (1986–2016) with properties and comprising of pervious a totaland of 11,324impervious time steps, areas along seen with in Table properties 4. of pervious and impervious areas seen in Table4.

TableTable 4. Design 4. Design attributes attributes of SUPERLOCAL.of SUPERLOCAL.

OverviewOverview

SurfacesSurfaces AreaArea (m2)(m 2) OtherOther Relevant Relevant Quantities

Public and private Soil infiltrationSoil infiltration rate estimated rate estimated at 0.10–0.15 Public and private pervious80008000 at 0.10–0.15 m/day in all pervious pervious m/day in all pervious areas areas RoofRoof 1543 1543 Public impervious (a) 8942 Public impervious (a) 8942 Buffers Storage (m3) Other Relevant Quantities Buffers Storage (m3) Roof- Other 60 Relevant m3, non-roof- Quantities 190 m3 , RWH buffers (b) 300 and mixed buffer 50 m3 Roof- 60 m3, non-roof- 190 m3, and mixed PotableRWH water buffers (DW) (b) buffer 300 50 Area 1000buffer m2, depth 50 m3 of 0.45 m, Water square 450 infiltration capacity 0.10–0.15 m/day Potable water (DW) GW buffer50 27 Filteredbuffer GW buffer 20 BW buffer 40 Area 1000 m2, depth of 0.45 m, infiltration Water square 450 Purification Treatment Cap (m3/day) Othercapacity Relevant 0.10–0.15 Quantities m/day

DWGW puriﬁcation buffer (c) 30 (mean) 27 /180 (max) Surface area of 400 m2 or smaller Helophyte ﬁlter (vertical) (d) 14.4 Filtered GW buffer 20 when aeration is added Vacuum pump 2.2

GWBW puriﬁcation buffer 7.2 (mean) 40 /15 (max)

Water 2019, 11, x FOR PEER REVIEW 12 of 26

Treatment Cap Purification Other Relevant Quantities (m3/day)

DW purification (c) 30 (mean)/180 (max)

Helophyte filter Surface area of 400 m2 or smaller when 14.4 (vertical) (d) aeration is added

Water 2019Vacuum, 11, 1227 pump 2.2 12 of 25

GW purification 7.2 (mean)/15 (max) A second step needed is to conceptualize the entire array of technologies seen in the integrated decentralizedA second systemstep needed of SUPERLOCAL is to conceptualize in the the ‘signal entire language’array of technologies used by UWOT, seen in which the integrated consists of demand decentralized system of SUPERLOCAL in the ‘signal language’ used by UWOT, which consists of and wastewater/runoff signals. This conceptualization can be seen in Figure4, which summarizes demand and wastewater/runoff signals. This conceptualization can be seen in Figure 4, which thesummarizes main components the main components seen in SUPERLOCAL, seen in SUPERL includingOCAL, including the household the household appliances, appliances, RWH RWH treatment unit, GWRtreatment unit unit, and GWR SUDS. unit UWOT and SUDS. is able UWOT to includeis able to include all of the all of aforementioned the aforementioned technologies; technologies; however, its architecturehowever, its focusesarchitecture on water focuses flows on water and isflows unable and to is capture unable nutrientto capture transport nutrient transport and recovery; and given this limitationrecovery; given (which this is limitation further (which discussed is further in Section discussed4), the in Section recovery 4), the properties recovery properties of the BW of stream the seen in FigureBW stream4 are seen not simulated,in Figure 4 are with no thet simulated, results focusingwith the results on treated focusing water on quantitiestreated water for quantities all streams instead. for all streams instead.

FigureFigure 4. 4.TheThe urban urban water water cycle cycle map of map SUPERLOC of SUPERLOCALAL in the signal in the language signal of language UWOT. of UWOT. 3.2. Application of the framework in SUPERLOCAL 3.2. Application of the framework in SUPERLOCAL TheThe developed developed performance performance assessment assessment framework framework along with along the with six identified the six identiﬁed KPIs is applied KPIs is applied inin thethe casecase ofof SUPERLOCAL,SUPERLOCAL, with with the the aim aim of ofevaluating evaluating the the two two design design options options of the of combined the combined RWH andRWH GWR and system.GWR system. Following Following the the ﬁrst first step step of of the the methodology, methodology,three three alternativealternative scenarios scenarios are are modeled inmodeled UWOT: in UWOT:

1.1. The first first baseline scenario scenario corresponds corresponds to to neig neighborhoodhborhood equivalent equivalent to SUPERLOCAL to SUPERLOCAL (i.e., (i.e., having having the same household types, occupancy and spatial characteristics) that however features the same household types, occupancy and spatial characteristics) that however features no no decentralized technologies. This reality reflects a neighborhood that follows the centralized modeldecentralized of linear technologies.water management, This where reality runoff reflects and a neighborhoodWW are propagated that followsdownstream the centralized(to the model outletof linear and watercentral management,stormwater or wastewater where runo servff andices),WW DW is are requested propagated from the downstream central mains (to the outlet and central stormwater or wastewater services), DW is requested from the central mains

and conventional appliances are used in all house types. This reality serves as the baseline for comparison and has the abbreviation of business-as-usual (BAU). 2. The second scenario is a neighborhood that features distinct recycling systems (RWH and GWR) that target different water uses. Treated RW covers all domestic uses, while treated GW covers common facilities, including a laundry and car wash. This decentralized reality has the abbreviation SCEN_A or “RWH|GWR”, to underline the distinct role of RWH and GWR. 3. The third scenario is a neighborhood where GWR usability is extended; in that case, treated GW acts as light RW and is directed to the same buffer unit as RW, in order to create a common pool that targets all water uses. This reality has the abbreviation SCEN_B or “RWH + GWR”, to Water 2019, 11, 1227 13 of 25 underline the combined role of RWH and GWR. All aforementioned scenarios are modeled with the present climate (i.e., historical rainfall data behaviorcaptured [ 44by]. the The gauge) UWOT and model with for the scenario social SCEN_A dimension (RWH defined|GWR) by can WML, be seen following in the upper local panel client of Figurebehavior5, while [44]. theThe UWOT UWOT model model for for SCEN_B scenario (RWH SCEN_A+ GWR) (RWH|GWR) can be seen can in be the seen lower in the panel upper of Figure panel5 . Basedof Figure on the 5, while results the of theUWOT model model runs, for the SCEN_B six defined (RWH KPIs + GWR) are calculated can be seen for SCEN_Ain the lower and panel SCEN_B; of inFigure the case 5. Based of reliability, on the results the KPI of is the applied model on runs two, the design six defined elements KPIs that are are calculated of interest for to SUPERLOCALSCEN_A and partners:SCEN_B; thein the treated case of water reliability, storage the unit KPI that is applie holdsd harvestedon two design (and elements reclaimed, that in are SCEN_B) of interest water to andSUPERLOCAL returns it back partners: to households, the treated as well water as the storage “water un square”,it that holds i.e., the harvested infiltration (and basin reclaimed, that receives in runoSCEN_B)ff from water pervious and returns areas as it well back as to overflow households, from as RWH. well as the “water square”, i.e., the infiltration basin that receives runoff from pervious areas as well as overflow from RWH.

Figure 5. Cont.

Water 2019, 11, 1227 14 of 25 Water 2019, 11, x FOR PEER REVIEW 14 of 26

FigureFigure 5. 5.UWOT UWOT topologiestopologies forfor the two design options SCEN_A and and SCEN_B. SCEN_B.

3.3.3.3. Model Model Validation Validation PriorPrior to to demonstrating demonstrating thethe resultsresults ofof thethe comparative performance assessment, it it is is important important to to validatevalidate modeling modeling results results against against real real data.data. This is possible for the the scenario scenario of of BAU BAU in in SUPERLOCAL, SUPERLOCAL, asas it it representsrepresents the conventional conventional reality reality that that can can be bechecked checked against against data data from from actual actual households households and andneighborhoods. neighborhoods. On the On demand the demand side, UWOT side, output UWOT from output the BAU from simulation the BAU can simulation be compared—in can be compared—interms of simulated terms daily of simulated aggregate daily household aggregate or neighborhood household or demands—to neighborhood the demands—to real demands the seen real demandsin Dutch seendrinking in Dutch water drinking networks, water which networks, are obta whichined from are obtained the high-resolution from the high-resolution data provided data by providedWML (see by Appendix WML (see A), Appendix as well Aas), a asthird well external as a third data external source data describing source describingnational average national per average capita perwater capita consumption water consumption [45] that is [45 not] that used is notto build used the to build model. the The model. results, The seen results, in Figure seen in 6 Figurefor the6 forhousehold the household (panel (panel(a)) as well (a)) asas wellthe whole as the neighbor whole neighborhoodhood scale (panel scale (b)), (panel show (b)), a deviation show a deviationbetween betweenUWOT BAU UWOT demand BAU demandoutputs outputsand WML and data WML that data is in that all cases is in allless cases than less2%. thanThis 2%.indicates This indicatesthat the thattranslation the translation of high-resolution of high-resolution data from data the DW from ut theility DW to the utility UWOT to the topologies UWOT has topologies been performed has been performedwell. Moreover, well. Moreover,the deviation the between deviation UWOT between model UWOT outputs model and outputs external and data external [45] falls data in [ 45the] fallsrange in theof range5%–8%. of 5–8%.This deviation This deviation is deemed is deemed reasonable, reasonable, considering considering that thatthe theexternal external source source describes describes a anational national average average instead instead of of regional regional sp specifics,ecifics, so so provincial provincial deviations deviations apply. apply. With regards to runoff, validation against real data is not possible as the area of SUPERLOCAL does not have a runoff gauge. However, insight on the expected runoff of the area can be obtained by comparing the UWOT BAU results with the results of another widely used rainfall-runoff conceptual model, such as the rational method [46], which links outlet runoff (discharge) Q with point (station) rainfall intensity i through the formula Q = C i A , where A is the catchment area and C is × × the so-called runoff coefficient, the single calibration parameter of the model. Depending on the time scale the rational method is used, C is typically referred to in literature either as event-based or annual runoff coefficient [47,48], which leads to an estimation of Q as the peak discharge (in m3/s) of an event or (mean) annual discharge (e.g., in m3/year), respectively, as a fraction of the total water received through precipitation i A . Due to the wide application of the rational method for preliminary × studies in catchments, value ranges of C for catchments with different spatial and soil properties exist, making this method easily applicable for rough estimations of runoff in case of ungauged catchments. To obtain a general, event-independent overview of the runoff response, the annual time scale has

Water 2019, 11, 1227 15 of 25 been applied for UWOT validation. The results show that runoﬀ produced by UWOT (which is on average 7032 m3/year) falls within the results obtained with the rational method for a range of C = 0.45–0.55 (panel (c) of Figure6). This range is deemed reasonable compared to values in engineering literature [49], given that the area of SUPERLOCAL features low density residential use and an almost Waterequal 2019 mixture, 11, x FOR of perviousPEER REVIEW and impervious areas, as seen in panel (d) of Figure6. 15 of 26

FigureFigure 6. 6. UWOTUWOT model model validation validation results. results. 3.4. Performance Assessment of SUPERLOCAL Decentralized Design Options With regards to runoff, validation against real data is not possible as the area of SUPERLOCAL does Thenot resultshave a ofrunoff the framework gauge. However, application insight in SUPERLOCAL on the expected are runoff summarized of the inarea Figure can 7be in obtained the form byof acomparing dashboard the focusing UWOT on resultsBAU results at a KPI with level the for results the two of design another options widely (SCEN_A used rainfall-runoff and SCEN_B), conceptualreadily understandable model, such as by the non-expert rational method groups. [46], Regarding which links the reductionoutlet runoff in household(discharge) consumption, Q with point (station)seen in panelrainfall (a) intensity of Figure i through7, a reduction the formula of demands 𝑄=𝐶𝑖𝐴 in the, where range A of is 32–58% the catchment is achievable area and for C isthe the neighborhood so-called runoff of SUPERLOCAL, coefficient, the depending single calibration on the mixture parameter of water-saving of the model. appliances Depending (and on thus the timethe household scale the rational type), with method the experimentalis used, C is typically housing referred units that to focusin literature on more either intensive as event-based water-aware or annualappliances runoff having coefficient the strongest [47,48], ewhichffect. Theleads eff toect an is estimation the same inof theQ as two the design peak discharge options (SCEN_A (in m3/s) vs.of anSCEN_B), event or as (mean) the mixture annual of discharge appliances (e.g., per householdin m3/year), type respectively, does not change. as a fraction The eff ofect the of eachtotal designwater receivedoption is, through however, precipitation different when 𝑖𝐴 one. looksDue atto thethewater wide askedapplication from—and of the returned rational to—the method central for preliminaryDW/WW utility studies (panel in catchments, (b) of Figure value7). The ranges average of C dailyfor catchments central DW with demand different of SUPERLOCAL,spatial and soil propertiesestimated atexist, 35.6 making m3/day inthis the method BAU scenario, easily applicable can be reduced for rough to 8.4 mestimations3/day for SCEN_A of runoff (RWH in case|GWR, of ungaugeddistinct) and catchments. even reduced To obtain further a general, to less event than-independent 1 m3/day for SCEN_Boverview (RWHof the runoff+ GWR, response, combined). the annualThe reduction time scale in thehas firstbeen case applied is by for approx. UWOT 76.5%, validation. while The the results reduction show in that the secondrunoff produced case reaches by UWOT (which is on average 7032 m3/year) falls within the results obtained with the rational method for a range of C = 0.45–0.55 (panel (c) of Figure 6). This range is deemed reasonable compared to values in engineering literature [49], given that the area of SUPERLOCAL features low density residential use and an almost equal mixture of pervious and impervious areas, as seen in panel (d) of Figure 6.

3.4. Performance Assessment of SUPERLOCAL Decentralized Design Options

Water 2019, 11, 1227 16 of 25

97.5%, achieving near complete autonomy from Drinking Water services with a combined RWH/GWR system that recycles most water uses. Likewise, signiﬁcant reduction are possible for the WW that propagates to the outlet: the generated WW that is estimated at 35.52 m3/day for the BAU case gets reduced to 20.62 m3/day (a reduction of approx. 42%) for SCEN_A and further reduced to 7.25 m3/day for SCEN_B, with a reduction of 79.6%. As expected, SCEN_B makes more eﬃcient use of treated GW byWater expanding 2019, 11, x itsFOR reusability PEER REVIEW and this has an impact on both reliance on central (external) DW, as17 wellof 26 as propagation of WW to the outlet.

Figure 7. Results of the framework for SUPERLOCAL. Figure 7. Results of the framework for SUPERLOCAL. This performance of these design options is reversed when one looks at the achieved % of runoff reduction3.5. Downscaling at an annual KPIs to scale Fit (panelthe Needs (d) of of Decision Figure7 ).Support SCEN_B now achieves a moderate annual average runoffThereduction results seen of 32%, in Figure while SCEN_A7 provide performsa lean-data significantly snapshot at better the level and of reaches abstraction a reduction needed of to 60%.communicate The reduction information rate is aforffected different by the circular dryness neighborhood of each year; fordesigns instance, to non-expert SCEN_A achieves groups. aHowever, rate of 66.1–69.6% the use of insimulation the five at driest a daily years, scale while gives runo the developedff reduction framework rate falls flexibility within the and range allows of 52.6–54.7%stakeholders for to the dig five deeper wettest in the years. response Likewise, and effi SCEN_Bciency of has decentralized a rate of 35.0–38.6% water systems. for the KPIs five driestcan be yearsdownscaled and 24.2–28.1% to a specific for the year, five season, wettest month years. or The even achieved at the %native of event-based daily scale runo via theirff reduction corresponding reveals a similar picture: SCEN_A reduces peak runoff during rainfall events significantly by 84% on average, output time series 𝛶 ∈ 𝑌⃗ to provide more detailed insight in the interplay between different urban againstwater streams, a moderate as averagewell as reductionthe dependence of 66% achievedof decentralized by SCEN_B. systems These on results inherently are reasonable, uncertain aslocal in thesources case of such the combinedas rainwater. design option (SCEN_B, RWH + GWR) treated GW and treated RW compete to coverFor the sameinstance, demand a more needs in-depth and use look a limited in system treated reliability water storage can space,be obtained thus leaving by looking less available at how storagedifferent for elements upcoming within runo theff events. system Similar respond findings and wh canen they be seen reach when failure one states. looks atFigure the reliability 8 shows the of bothresults systems from the in terms last year of treated of simulation storage in bu SCEN_A,ffer (panel where (c) of one Figure can7 see); SCEN_B the system combines response both in runoff local treatmentthat surpasses units RWH at a cost (panel of reliability (b)), in runoff on available stored in treated the infiltration water storage, basin (panel which (c)), falls as from well 87.5%as runoff to 82.5%.that escapes Both reliability both of these levels retention are reasonable schemes and and in themanages range to of highoverflow values at forthe RWHoutlet schemes (panel (d) [50 of]. TheFigure reliability 8). Infiltration of the infiltration capacity of basin the unit (“water is exc square”)eeded twice, is found with toboth be events very high occurring in both from scenarios heavy precipitation events occurring on May and June 2016. In the second case, the storage of 400 m3 is enough to absorb this capacity exceedance; in the first case, the storage limit is surpassed, leading to

a single overflow event that is marked as an inefficiency of the infiltration system 𝑌. Likewise, other KPIs can be downscaled to a daily scale to observe system response in detail. Having for example autonomy in mind, Figure 9 shows the percentage of total DW demand asked from the central DW service over time for the last year of simulation, with a value of zero indicating that the system covered all of its demand locally during that day. Both topologies achieve autonomy from the central services which is in principle dependent on the rainfall that the area receives, seen in the upper panel

Water 2019, 11, 1227 17 of 25

(>99%), with its design capacity being exceeded only in three out of the 11,324 total simulation time steps. This finding is in line with the design requirements in SUPERLOCAL, which aim at capturing all rainwater locally, rather than use infiltration to reduce flows from frequent events only. The aforementioned KPIs can be pooled together in a form of a radar plot (panel (e) of Figure7) that captures all dimensions of the improvements in urban water cycle services towards circularity that are achievable with these two design options. What is evident in this figure is the tradeoff between autonomy (i.e., water asked from and pushed to centralized networks) and flood-proofing; SCEN_B combines RWH and GWR together and leads to a more autonomous decentralized system, but at a cost of flood retaining capacity, which is higher in case RWH and GWR are distinct.

3.5. Downscaling KPIs to Fit the Needs of Decision Support The results seen in Figure7 provide a lean-data snapshot at the level of abstraction needed to communicate information for different circular neighborhood designs to non-expert groups. However, the use of simulation at a daily scale gives the developed framework flexibility and allows stakeholders to dig deeper in the response and efficiency of decentralized water systems. KPIs can be downscaled to a specific year, season, month or even at the native daily scale via their corresponding output time series Υ →Y to provide more detailed insight in the interplay between different urban i ∈ water streams, as well as the dependence of decentralized systems on inherently uncertain local sources such as rainwater. For instance, a more in-depth look in system reliability can be obtained by looking at how different elements within the system respond and when they reach failure states. Figure8 shows the results from the last year of simulation in SCEN_A, where one can see the system response in runoff that surpasses RWH (panel (b)), in runoff stored in the infiltration basin (panel (c)), as well as runoff that escapes both of these retention schemes and manages to overflow at the outlet (panel (d) of Figure8). Infiltration capacity of the unit is exceeded twice, with both events occurring from heavy precipitation events occurring on May and June 2016. In the second case, the storage of 400 m3 is enough to absorb this capacity exceedance; in the first case, the storage limit is surpassed, leading to a single overflow event that is marked as an inefficiency of the infiltration system Y f . Likewise, other KPIs can be downscaled to a daily scale to observe system response in detail. Having for example autonomy in mind, Figure9 shows the percentage of total DW demand asked from the central DW service over time for the last year of simulation, with a value of zero indicating that the system covered all of its demand locally during that day. Both topologies achieve autonomy from the central services which is in principle dependent on the rainfall that the area receives, seen in the upper panel of Figure9. The dependence on wet and dry spells is evident, with SCEN_A showing higher sensitivity, while SCEN_B showing better autonomy and robustness, as rainwater is supplemented with another steady source of water (treated GW). Insights can be also obtained for the runoff reduction KPI by downscaling results to an event scale, i.e., to the peak runoff observed during a precipitation event [47]. Since UWOT is forced by the same time series in all topologies, a reduction in runoff peaks is observed for the same events over time among these different realities; runoff peaks can be then compared between the conventional (BAU) topology that has no active RWH interventions and the SUPERLOCAL scenarios (SCEN_A and SCEN_B). This has been implicitly done when the KPI of event-based runoff reduction was introduced, but this provided a single (average) quantity at a finer scale, instead of richer information on event-based reduction. To demonstrate a more detailed approach, runoff time-series generated by UWOT are analysed on an event scale and plotted in a comparative scatter plot (panel (a) of Figure 10), where daily runoff of the current reality (BAU neighborhood) is plotted against daily runoff of an alternative reality which has one of the SUPERLOCAL design options (SCEN_A and SCEN_B). The results are plotted in a double logarithmic scale in order to capture the runoff value range including rare events and plotted against line x = y which represents the case where runoff from a SUPERLOCAL reality QSCEN is equal to runoff in BAU QBAU, there is no change in the peak runoff response of the catchment). Water 2019, 11, x FOR PEER REVIEW 18 of 26

Waterof Figure2019, 11 ,9. 1227 The dependence on wet and dry spells is evident, with SCEN_A showing 18higher of 25 sensitivity,Water 2019, 11while, x FOR SCEN_B PEER REVIEW showing better autonomy and robustness, as rainwater is supplemented18 of 26 with another steady source of water (treated GW). Theof resultsFigure of 9. a comparativeThe dependence analysis on betweenwet and adry SUPERLOCAL spells is evident, reality with and BAUSCEN_A come showing in the form higher of a cloudsensitivity, of data while points SCEN_B(QBAU, QshowingSCEN); the better further autonomy they lie and from robustness,x = y, the as more rainwater eﬃcient is RWH supplemented was for thatwith particular another event. steady source of water (treated GW).

Figure 8. Response of the infiltration unit of SCEN_A during simulation. Figure 8. Response of the inﬁltration unit of SCEN_A during simulation.

Rainfall [mm/hr] 100 Figure 8. Response of the infiltration unit of SCEN_A during simulation. 75 50 25 0 Rainfall [mm/hr] 100 75 Percentage of total demand asked from central DW service 100% 50 90% 25 80% 0 70% 60% Percentage of total demand asked from central DW service 50%100% 40%90% 30%80% 20%70% 10%60% 0%50% 1-1-201640% 1-2-2016 3-3-2016 3-4-2016 4-5-2016 4-6-2016 5-7-2016 5-8-2016 5-9-2016 6-10-2016 6-11-2016 7-12-2016 30% Date BAU SCEN_A SCEN_B 20% 10% Figure 9. Time-series of the decentralized system DW autonomy for both design options. 0% 1-1-2016Figure 1-2-2016 9. Time-series 3-3-2016 3-4-2016of the decentralized 4-5-2016 4-6-2016 system 5-7-2016 DW autonomy 5-8-2016 5-9-2016 for both 6-10-2016 design 6-11-2016 options. 7-12-2016 The results of Figure10o ffer high-resolutionDate insight on the interactionBAU betweenSCEN_A design detailsSCEN_B of the combinedInsights can RWH be andalso GWR obtained system for andthe runoff the hydrological reduction regime.KPI by downscaling In every case, results the cloud to an of event data pointsscale, i.e., follows toFigure the a bell-shapedpeak 9. Time-series runoff figureobserved of the that decentralized during asymptotically a pr ecipitationsystem leads DW toeventautonomyx = [47].y forfor Since both very UWOT design large runooptions.is forcedff volumes by the (verysame raretime events). series in Lower all topologies, runoff volumes a reduction are considerably in runoff peaks reduced is observed and, for many for the events, same absorbed events over up Insights can be also obtained for the runoff reduction KPI by downscaling results to an event totime the among maximum these possible different reduction realities; by runoff the SUPERLOCAL peaks can be then system, compared as indicated between by the setconventional of lowest scale, i.e., to the peak runoff observed during a precipitation event [47]. Since UWOT is forced by the points(BAU) in topology area (see that numbered has no areaactive (1) RWH of panel interventions (a)). A plateau and ofthe points SUPERLOCAL indicating scenarios an upper threshold(SCEN_A same time series in all topologies, a reduction in runoff peaks is observed for the same events over inand reduction SCEN_B). is alsoThis evident has been (numbered implicitly area done (2) wh ofen panel the (a));KPI uponof event-based closer inspection, runoff reduction this equals was to time among these different realities; runoff peaks can be then compared between the conventional yintroduced,= Q but= thisk providedD, where ak singleis the (average) treatment quantity capacity at ofa finer the RWH scale, unitinstead and of D richer is the information daily flux (BAU)SCEN topology −that has no active RWH interventions and the SUPERLOCAL scenarios (SCEN_A ofon abstracted event-based water reduction. demands To demonstrate from the treated a more storage detailed unit, approach, equal to 20.3runoff m 3time-series/day in SUPERLOCAL. generated by and SCEN_B). This has been implicitly done when the KPI of event-based runoff reduction was TheseUWOT points are analysed reflect events on an happeningevent scale atand a timeplotted where in a water comparative can be treatedscatter plot but cannot(panel be(a) storedof Figure in introduced, but this provided a single (average) quantity at a finer scale, instead of richer information the10), system—i.e., where daily thererunoff is of no the storage current in reality the potable (BAU water neighborhood) buffer left. is In plotted that case, against part daily of the runoff treated of on event-based reduction. To demonstrate a more detailed approach, runoff time-series generated by wateran alternative covers the reality daily demandswhich has for one that of daythe andSUPERLOCAL the rest overflows design fromoptions the RWH(SCEN_A unit. and Besides SCEN_B). these UWOT are analysed on an event scale and plotted in a comparative scatter plot (panel (a) of Figure flatThe plateaus, results are an asymptoticplotted in a envelope double oflogarithmic maximum sc distanceale in order from theto capture curve can the be runoff seen invalue area (3)range of 10), where daily runoff of the current reality 𝑥=𝑦(BAU neighborhood) is plotted against daily runoff of panelincluding (a) in rare Figure events 10; this and corresponds plotted against to points line of maximum which e representsfficiency for the the case RWH where system, runoff i.e., from where a an alternative reality which has one of the SUPERLOCAL design options (SCEN_A and SCEN_B). storage tanks are empty and able to absorb the maximum amount of runoff. A mirrored envelope The results are plotted in a double logarithmic scale in order to capture the runoff value range including rare events and plotted against line 𝑥=𝑦 which represents the case where runoff from a

Water 2019, 11, 1227 19 of 25

alsoWater exists 2019, 11 closer, x FOR to PEER line REVIEWx = y (numbered area (4) in panel (a)) that can be perceived as the analogue19 of 26 of minimum efficiency for the decentralized RWH system. A comparison of SCEN_A (RWH|GWR, distinct)SUPERLOCAL and SCEN_B reality (RWH 𝑄+ isGWR, equal combined), to runoff in seen BAU in panel𝑄, (b)there of Figureis no change 10, off ersin the high-resolution peak runoff resultsresponse that of are the in catchment). line with Figure The 7results: SCEN_A of a ocomparativeffers a more consistentanalysis between picture ofa SUPERLOCAL runoff reduction, reality with aand maximum BAU come efficiency in the curveform of further a cloud from of xdata= ypointsand a (𝑄 lower,𝑄 spread); ofthe points further between they lie the from maximum 𝑥=𝑦, andthe more minimum efficient effi ciencyRWH was curves. for that particular event.

Figure 10. Event-based runoﬀ reduction for diﬀerent SUPERLOCAL design options and attributes.

4. DiscussionFigure 10. Event-based runoff reduction for different SUPERLOCAL design options and attributes.

TheThe previousresults of sectionFigure has10 offer shown high-resolution key results from insight the on application the interaction of the between developed design framework details inof the combined RWH RWH and and GWR GWR system system and of SUPERLOCAL.the hydrological Arguably, regime. In the every focus case, was the on cloud comparing of data scenariospoints follows of ‘technological a bell-shaped realities’ figure withthat asymptotically different design leads options to against𝑥=𝑦 for the very linear, large centralized runoff volumes system (BAU);(very rare the events). present Lower climate runoff conditions volumes that are drive considerably rainfall remain reduced unchanged and, for many (i.e., historicalevents, absorbed rainfall dataup to were the maximum used as-is), possible while thereduction assumptions by the SUPERL on clientOCAL behavior system, (occupancy as indicated patterns by the and set frequency of lowest ofpoints use) in were area the(see same numbered for each area run. (1) of However, panel (a)). the A plateau developed of points framework indicating is broader an upper and threshold allows thein reduction formulation is ofalso di ffevidenterent scenarios (numbered where area climate (2) of and panel society (a)); also upon changes closer (Figure inspection,3). This this will equals impact to the performance of the decentralized system, thus leading to a different result picture at both coarse 𝑦=𝑄 =𝑘𝐷, where k is the treatment capacity of the RWH unit and D is the daily flux of andabstracted fine level. water A common demands question from the regarding treated change,storagefor unit, instance, equal isto whether 20.3 m3 integrated/day in SUPERLOCAL. decentralized technologiesThese points suchreflect as SUPERLOCALevents happening have at thea time capacity where to water future-proof can be neighborhoodstreated but cannot against be stored flooding. in the system—i.e., there is no storage in the potable water buffer left. In that case, part of the treated water covers the daily demands for that day and the rest overflows from the RWH unit. Besides these flat plateaus, an asymptotic envelope of maximum distance from the curve can be seen in area (3) of panel (a) in Figure 10; this corresponds to points of maximum efficiency for the RWH system, i.e.,

Water 2019, 11, 1227 20 of 25

To demonstrate this through the simulation-based framework, let us hypothesize a possible future that has higher rainfall, based on a relevant KNMI report on climate adaptation [51]. To study the effect of these future rainfall patterns to the SUPERLOCAL integrated system, we can formulate three future scenarios, with and without decentralized technologies (BAU, SCEN_A and SCEN_B) where the climate has changed and plot runoff reduction results at an event scale. The results, plotted in Figure 11 against the corresponding present BAU scenario (excluding small runoff events in [0.1,10] for clarity) reveal that while the future holds multiple more intense runoff for BAU (i.e., points above the x = y line), this increased runoff intensity is mitigated when integrated decentralized systems are used: both solutions (SCEN_A and SCEN_B) lead to a picture of reduced runoff, with SCEN_A giving a more consistent picture that, despite the worsening hydrological regime, leads to lower runoff values Waterthan 2019 the, 11 present, x FOR PEER BAU REVIEW response (Figure 11). Similar results can be obtained in case of less21 rainfall of 26 (a drier future), but also in case social behavior scenarios need to be taken into account as well.

Figure 11. Event-based runoff reduction for a future reality and for both design options. Figure 11. Event-based runoff reduction for a future reality and for both design options. Besides (re-)forming scenarios, decision-makers can return back to step (3) of Figure3 to refine the designIn spite of of their the flexibility, chosen decentralized the demonstrated system fram andework see the doesn’t difference come in without the results. limitations. Design attributes As seen insuch studies as the with treatment metabolism capacity models and storage [39,55], of KPIs RWH re orly GWRon the units model can used be changed, and are withthus thesebound changes to its limitations.being reflected For instance, on the coarse the inclusion or fine scale of water of the quality results. indicators To demonstrate is more this,challenging a number in ofUWOT, different as watertreatment quality options modeling (25 m functions3/day, 180 are m 3limited/day) are and simulated need external for SCEN_A data on alongthe treatment with the units. default This design also appliesof 48 m for3/day, other with concepts the runo importantff reduction to ofcircular each corresponding water, such as design energy being consumption plotted at anand event nutrient scale reclamation;(see panels [c]the andlatter [d] is also of Figure of particular 10). Evidently, value to SUPERLOCAL, higher treatment as optionsseen with lead the tolocal a more BW stream potent treatmentevent-based (Figure runo ff4)response and could with thus higher form operationalthe focal point plateaus of future (numbered research area and (2)model of panel improvements. (a)) and less Moreover,spread, even the though financial the dimension very large treatment (i.e., capital option and (180 operational m3/day) hascosts) reduced is not maximum included easffi aciency, scenario as it dimensionis perhaps in disproportionally Figure 3, but can large be considered compared in to future the (constant) iterations storage of the model. volume These of 50 limitations m3. The ability spark to ideas for future research→ towards an integrated framework that will explore more externalities, but recalculate KPIs f (Y) for an array of different parameters µi →µ points to an optimization problem, with also include the interplay between water and energy or∈ water and nutrients. Another point of f (→Y) being the objective function and →µ being the control variable space. It would be thus interesting consideration is that the current application cannot give probabilistic results with high confidence, as for future research to expand the ability of the framework to (re-)design attributes for a given reality it is based on a relatively small dataset of rainfall (31 years); this can be methodologically improved and expand the rationale to a combined simulation-optimization framework [52–54] by automating by running UWOT with an ensemble of synthetic rainfall events, generated with the use of a suitable the return to step (3) of Figure3, in order to address the optimal design of decentralized systems. stochastic model [56]. One has to also consider that the results seen in Figure 7 are based on an In spite of the flexibility, the demonstrated framework doesn’t come without limitations. As seen idealized picture of a given decentralized system design—operational aspects such as maintenance in studies with metabolism models [39,55], KPIs rely on the model used and are thus bound to its frequency, downtime, accidents, variable sub-daily treatment rates etc. are not considered. However, limitations. For instance, the inclusion of water quality indicators is more challenging in UWOT, some of these aspects can be modeled by altering the UWOT topologies [57], provided that data exist as water quality modeling functions are limited and need external data on the treatment units. This also to supplement the added structural complexity. In case they are not modeled, the results can be applies for other concepts important to circular water, such as energy consumption and nutrient considered as an idealized ‘upper limit’ of efficiency and autonomy that can be achieved for a proposed decentralized system architecture. Finally, the demonstrated framework is limited to the neighborhood scale in order to be applied to SUPERLOCAL, but can be also applied to larger scales, for instance to large blue-green area design [20], or even to a whole city level [21], possibly with changes to the KPIs to fit the scope of the application.

Water 2019, 11, 1227 21 of 25 reclamation; the latter is also of particular value to SUPERLOCAL, as seen with the local BW stream treatment (Figure4) and could thus form the focal point of future research and model improvements. Moreover, the financial dimension (i.e., capital and operational costs) is not included as a scenario dimension in Figure3, but can be considered in future iterations of the model. These limitations spark ideas for future research towards an integrated framework that will explore more externalities, but also include the interplay between water and energy or water and nutrients. Another point of consideration is that the current application cannot give probabilistic results with high confidence, as it is based on a relatively small dataset of rainfall (31 years); this can be methodologically improved by running UWOT with an ensemble of synthetic rainfall events, generated with the use of a suitable stochastic model [56]. One has to also consider that the results seen in Figure7 are based on an idealized picture of a given decentralized system design—operational aspects such as maintenance frequency, downtime, accidents, variable sub-daily treatment rates etc. are not considered. However, some of these aspects can be modeled by altering the UWOT topologies [57], provided that data exist to supplement the added structural complexity. In case they are not modeled, the results can be considered as an idealized ‘upper limit’ of efficiency and autonomy that can be achieved for a proposed decentralized system architecture. Finally, the demonstrated framework is limited to the neighborhood scale in order to be applied to SUPERLOCAL, but can be also applied to larger scales, for instance to large blue-green area design [20], or even to a whole city level [21], possibly with changes to the KPIs to fit the scope of the application.

5. Conclusions A framework for the ex ante evaluation of integrated decentralized systems at a neighborhood scale, based on the simulation testbed of an urban water cycle model, has been demonstrated in this study. The framework is applicable to integrated neighborhood projects that include an array of distributed options, including water-saving appliances, household or neighborhood-scale RWH and/or GWR as well as sustainable drainage options (SUDS), while being able to simulate the whole system response and convey performance information at both coarse (KPI) and fine scale. The use of this framework aims at providing quantitative insight on multiple urban water cycle domains which can be used to support decisions on circular water options in neighborhoods. The framework is applied in the case of SUPERLOCAL, a circular neighborhood designed in Limburg, in order to assess and compare the performance of two different design options: an integrated decentralized system featuring distinct RWH and GWR units (SCEN_A), as well as a system that features the same design attributes but combines these systems together to target the same household water demands (SCEN_B). 31 years of daily rainfall data are used to generate results at both high abstraction level (Figure7) as well as in more detail (Figure 10), depending on decision-making needs. The results of the application show that SUPERLOCAL has the potential of being a smart, autonomous water neighborhood solution, achieving a reduction of 32.1–58.2% in household water demand, depending on the appliance technologies used at each household type. Moreover, it will lessen the dependence to 1 drinking water provided by centralized services to at most 4 of the water requested in a conventional neighborhood. A significant reduction in annual runoff can be expected as well, amounting to approx. 60% for the split RWH/GWR system (SCEN_A) and 32% for the combined RWH/GWR scheme (SCEN_B), compared to the case without any RWH scheme (BAU). The results fall in line with the findings of other studies of RWH and GWR systems; for instance, other studies report an efficiency of residential RWH systems in the range of 5.6–55.0% in terms of water savings [58–61], depending on regional climate and harvesting scale, with lower values in drier climates and smaller rooftop areas and higher values (33.8–52.5%) in humid climates [59]; the latter are similar to the climate type of SUPERLOCAL. Studies on combined RWH-GWR systems report an efficiency on potable water savings in the range of 36.7–42.0% [62], with systems relying only on GWR being more efficient. Likewise, the reliability levels estimated in this study are on par with past literature [50]. Water 2019, 11, x FOR PEER REVIEW 6 of 26 Water 2019, 11, 1227 22 of 25 longer time and is reused. This localization is based on employing neighborhood-scale harvesting, recycling and energy & material recovery technologies. As part of the proposed designs, two options are being considered as potential operational As a general remark, the results indicate a tradeoff between systemsystems autonomy for SUPERLOCAL. (i.e., water In the first asked design option, greywater is treated locally and is then used to from and pushed to centralized networks) and rainwater retentioncover/runo specificff reduction shared water that demands, is evident such as a common laundry and car wash facility, while when the two solutions (SCEN_A and SCEN_B) are compared (panelrainwater (e) of Figure is captured7). Split and systemstreated locally with to cover the rest of the DW demands. This is the most likely distinct RW/GW streams, such as the one seen in SCEN_A, are efficientcase in flood-proofingto be implemented neighborhoods in SUPERLOCAL, with two distinct local treatment systems (RWH and GWR) targeting different water uses. The second option that is considered extends GW reusability and keeping runoff locally but are less efficient at providing a constantby stream considering of reused treated GW water as having to fit localthe same quality characteristics as rainwater following a natural needs; they have been also found more sensitive, in terms of their waterfiltering reuse step, ethusfficiency, being able to dryto be spells. mixed together with RW in a shared buffer and thus target all Combined systems, such as the one seen in SCEN_B, are more consistentdomestic in water their uses. water This reuse option and combines thus the two local treatment systems and is a more integrated experimental alternative that will be considered only if the treated GW quality of the project pilot is autonomy from central networks, but o er less available storage for high-intensity events and reduced ff found to be high enough. Both design options are complemented with the same use of water-saving climate-proofing capability. To reach the efficiency in runoff reductionappliances of SCEN_A, on a household a neighborhood scale, with the exact mixture of appliances being dependent on the focusing on SCEN_B needs to invest in largest storage for treated water,household which type comes (Table 2). at a higher cost. Beyond the application in SUPERLOCAL, it is argued that this framework is readily applicable for the strategic evaluation of circular neighborhood designs with different architectures and at different scales and climates. Building on the expertise of past studies, the framework can be upscaled for the strategic evaluation of circular city designs as well, in order to be part of a resilience assessment toolbox for cities, as envisioned in Nikolopoulos et al. [21] and Makropoulos et al. [57]. This type of study is envisaged to provide, along with supplementary tools on the strategic, tactical and operational scale, a solid base for the design and strategic evaluation of integrated decentralized systems as parts of a broader circular water management strategy, thus enabling the transition towards a circular and more sustainable future for urban water.

Author Contributions: Conceptualization, D.B. and D.v.D.; Data curation, D.B. and D.v.D., Formal analysis, D.B., D.v.D. and C.M., Funding acquisition, H.-J.v.A. and J.F., Investigation: D.B., D.v.D., H.-J.v.A., J.F., D.N. and C.M., Methodology: D.B., Project administration: H.-J.v.A. and J.F., Software: D.B. and D.N., Supervision: C.M., Validation: D.B. and D.v.D., Visualization: D.v.D. and D.B., Writing—original draft: D.B., Writing—review & editing: D.v.D., J.F., C.M. Funding: This paper was based on research ﬁnanced by the joint research programme BTO-WiCE that KWR carries out for the Dutch drinking water companies and De Watergroep, Flanders, and by the EC LIFE project Local Water Adapt with project reference LIFE17 CCA/NL/000043.

Conflicts of Interest: The authors declare no conflict of interest. Figure 2. Overview of the case study and household types of SUPERLOCAL. Disclaimer: This paper is an ex ante preliminary assessment of the SUPERLOCAL water system, where simplifications have been made for research purposes due to available model options. Data andTable dimensions 2. The case usedstudy and household types of SUPERLOCAL. Water 2019, 11, x FOR PEER REVIEW 7 of 26 for the case study of SUPERLOCAL are preliminary design parameters that are being considered which might Experimental differ in the actual site implementation. Hence, no claims can be made from this studyUnit on the actualHouses function of Apartments Total HousesWater the system when it is set into place. Type of shower (n) Recirculation (3) Water saving (113) 129 saving (13) Abbreviation (a) (b) (c) Appendix A (Figure 2) Type washing 50% regular (56); 50% use Regular (13) Regular (3) 77 machine (n) shared launderette (5) Number of dwellings 13 3 113 129 Food grinder Water Large shared water saving Table A1. Water usage and per capita consumption on SUPERLOCALPersons/dwelling appliances. Water saving (3) 25 (n)2.2 (29) saving1.8 (5) (13) 2.0 (226) on each260 level (9) Water 2019, 11, x FOR PEER REVIEW(persons) 7 of 26 Technology Drinking Water Usage Discharge Tap(s) (n) Regular (39) Regular (6) Regular (226) 271 Frequency Per Capita Type of toilet Silent Standard Water Usage d Water Silent vacuum (113) 129 of Use f Demand Request (L/use/day) t Type of shower GW(n) (n)BW SoilOutdoor (PerviousRecirculationvacuum tap Areas) (n) (13) (3) vacuumRegularWater (3)(13) saving (113)Regular (3) 129 - (0) 16 (uses/day) (L/person/day) saving (13) Waste streams: = BW sewage, = GW sewage, = In soil Bathroom Type washing 50% regular (56); 50% use Regular (13) Regular (3) 77 Conventional shower 8.7 8.1 70.5machine (n) 100% 0% 0% shared launderette (5) Watersaving shower 6.9 8.1 55.9 100% 0%2.3. Formulating 0% the Performance Assessment Chain for Decentralized Urban Water Systems Recirculation shower 2.5 8.1 20.3Food grinder 100% 0%Water 0% Large shared water saving UsingWater a savingsimulation (3) testbed such as UWOT provides25 a detailed picture of the whole system Sink 2 2.5 5.0(n) 100%saving 0% (13) 0% on each level (9) response—in terms of requested DW, produced WW and simulated runoff—for a given design Toilet Tap(s) (n) Regular (39)option thatRegular encompasses (6) all selectedRegular technologies (226) on a household271 and neighborhood scale. This Toilet 6 6 36.0 100% 0%design option 0% could be considered a scenario in the neighborhood, encompassing assumptions on Vacuum toilet (silent and standard) 1 6Outdoor 6.0 tap (n) 0% Regular 100% (13) 0%Regular (3) - (0) 16 the mixture, usage and design attributes of technologies, but also implicitly including assumptions Waste streams: on = BWclimate sewage, (through = theGW use sewage, of rainfall =data In soil to generate runoff) and client behavior (by assuming a set occupancy and frequency of use). Likewise, a neighborhood that features the same spatial and 2.3. Formulating the Performance Assessmentoccupancy Chain characteristics for Decentralized but Urban no water-saving Water Systems or decentralized measures, i.e., a unit of linear water management that requests DW from the central networks and quickly disposes WW and runoff to Using a simulation testbed suchcorresponding as UWOT provides central services, a detailed could picture be considered of the whole another system scenario able to be modeled with UWOT. response—in terms of requested DW,It becomes produced thus WW evident and thatsimulated UWOT runoff—for is able to model a given different design ‘realms’ in the form of scenarios that option that encompasses all selectedrepresent technologies management on a household choices, andclimate neighborhood characteristics scale. and This social behaviors through its required design option could be considered ainputs scenario and in component the neighborhood, topology encompassing(i.e., house types, assumptions RWH and/or on GWR treatment unit). A basis for a the mixture, usage and design attributessimulation-based of technologies, assessment but also framework implicitly includingcould then assumptions start with the following conceptualization: “One on climate (through the use of rainfallcould data assessto generate the performance runoff) and clientof a decentralized behavior (by assumingsystem by a comparingset the ‘decentralized’ scenario occupancy and frequency of use). withLikewise, the currenta neighborhood reality, i.e., that baseline features scenario, the same that spatial reflects and linear management and relies on occupancy characteristics but no water-savingcentralized or networks.” decentralized measures, i.e., a unit of linear water management that requests DW from the Thiscentral conceptualization, networks and quickly however, disposes has a caveat: WW and performance runoff to assessment is directly dependent on corresponding central services, couldthe be outputconsidered of the another model, scenario which ablegenerates to be modeled a wealth with of data UWOT. on daily time steps. There needs to be a It becomes thus evident that UWOThigher is able level to model of abstraction different ‘realms’from (complex) in the form data ofprod scenariosuced by that the model to the (lean) data needed to represent management choices, climateevaluate characteristics performance and and social reach behaviors decisions. throughThis abstraction its required can be reached by introducing a number inputs and component topology (i.e.,of house key performance types, RWH indicatorsand/or GWR (KPIs), treatment which unit). repr Aesent basis characteristic for a performance metrics of the simulation-based assessment frameworksystem. could then start with the following conceptualization: “One could assess the performance of a decentralizedThe use ofsy KPIsstem to by convey comparing information the ‘decentralized’ is considered scenario important, as they can be viewed as a crucial with the current reality, i.e., baselinelink betweenscenario, assessmentthat reflects frameworks linear management and the decision and relies-making on process, since they distil the—often centralized networks.” complex—information of assessment methodologies such as modeling-based frameworks in simple This conceptualization, however,quantities, has a caveat: able pe torformance be interpreted assessment efficiently is directly and quickly dependent by non-experts on such as management units the output of the model, which generatesand stakeholders a wealth of [33].data Identifyingon daily time efficient steps. KPIsThere fo needsr each to decision-making be a domain is no trivial task higher level of abstraction from (complex)and is thedata subject produced of extensive by the model research to thein multiple(lean) data fields needed [34,35], to including urban water [36–39]. The evaluate performance and reach decisions.non-trivial This nature abstraction of forming can be KPIsreached comes by introducingfrom the interaction a number of multiple factors that affect design, of key performance indicators (KPIs),such which as: (a) repr the esentproject characteristic aim and stakeholder performance target metrics groups, of (b) the the assessment framework and model system. capabilities, as the KPIs need to be derived from the data output generated by the model, (c) the needs The use of KPIs to convey informationof decision-makers, is considered asimportant, the KPIs asneed they to can include be viewed multiple as a systemcrucial aspects that affect decision-making, link between assessment frameworksbe and understandable the decision -makingand, to theprocess, greatest since possible they distil extent, the—often co-designed with the interested end-users. complex—information of assessmentWith methodologies regards to thissuch consideration, as modeling-based the simulati frameworkson-based in simple assessment framework conceptualization quantities, able to be interpreted efficientlycan be rephrasedand quickly as by follows: non-experts “One such may as assess management the performance units of a decentralized system by and stakeholders [33]. Identifying efficientcomparing KPIs the fo r‘decentralized’ each decision-making scenario domainwith the is baselin no triviale scenario task (current reality) that reflects linear and is the subject of extensive researchmanagement in multiple and fields relies [34,35], on centrali includingzed technologiesurban water via[36–39]. a number The of Key Performance Indicators.” non-trivial nature of forming KPIs comes from the interaction of multiple factors that affect design, such as: (a) the project aim and stakeholder target groups, (b) the assessment framework and model capabilities, as the KPIs need to be derived from the data output generated by the model, (c) the needs of decision-makers, as the KPIs need to include multiple system aspects that affect decision-making, be understandable and, to the greatest possible extent, co-designed with the interested end-users. With regards to this consideration, the simulation-based assessment framework conceptualization can be rephrased as follows: “One may assess the performance of a decentralized system by comparing the ‘decentralized’ scenario with the baseline scenario (current reality) that reflects linear management and relies on centralized technologies via a number of Key Performance Indicators.”

Water 2019, 11, x FOR PEER REVIEW 6 of 26

longer time and is reused. This localization is based on employing neighborhood-scale harvesting, recycling and energy & material recovery technologies. As part of the proposed designs, two options are being considered as potential operational systems for SUPERLOCAL. In the first design option, greywater is treated locally and is then used to cover specific shared water demands, such as a common laundry and car wash facility, while rainwater is captured and treated locally to cover the rest of the DW demands. This is the most likely case to be implemented in SUPERLOCAL, with two distinct local treatment systems (RWH and GWR) targeting different water uses. The second option that is considered extends GW reusability by considering treated GW as having the same quality characteristics as rainwater following a natural filtering step, thus being able to be mixed together with RW in a shared buffer and thus target all domestic water uses. This option combines the two local treatment systems and is a more integrated experimental alternative that will be considered only if the treated GW quality of the project pilot is found to be high enough. Both design options are complemented with the same use of water-saving appliances on a household scale, with the exact mixture of appliances being dependent on the household type (Table 2).

Figure 2. Overview of the case study and household types of SUPERLOCAL.

Table 2. The case study and household types of SUPERLOCAL. Water 2019, 11, x FOR PEER REVIEW 7 of 26 Experimental Unit Houses Apartments Total HousesWater Type of shower (n) Recirculation (3) Water saving (113) 129 saving (13) Abbreviation Water 2019, 11, 1227 (a) 23 of 25 (b) (c) (Figure 2) Type washing 50% regular (56); 50% use Regular (13) Regular (3) 77 machine (n) shared launderette (5) Number of dwellings 13 3 113 129 Food grinder Water Large shared water saving Table A1. Cont. Persons/dwelling Water saving (3) 25 (n)2.2 (29) saving1.8 (5) (13) 2.0 (226) on each260 level (9) Water 2019, 11, x FOR PEER REVIEW(persons) 7 of 26 Technology Drinking Water Usage Discharge Tap(s) (n) Regular (39) Regular (6) Regular (226) 271 Frequency Per Capita Type of toilet Silent Standard Water Usage d Water Silent vacuum (113) 129 of Use f Demand Request (L/use/day) t Type of shower GW(n) (n)BW SoilOutdoor (PerviousRecirculationvacuum tap Areas) (n) (13) (3) vacuumRegularWater (3)(13) saving (113)Regular (3) 129 - (0) 16 (uses/day) (L/person/day) saving (13) Waste streams: = BW sewage, = GW sewage, = In soil Kitchen Type washing 50% regular (56); 50% use Regular (13) Regular (3) 77 Food grinder 3 0.33machine 1.0 (n) 0% 100% 0% shared launderette (5) Cooking 1 1.4 1.4 100% 0%2.3. Formulating 0% the Performance Assessment Chain for Decentralized Urban Water Systems Dishwashing by hand 9.1 0.4 3.6Food grinder 100% 0%Water 0% Large shared water saving UsingWater a savingsimulation (3) testbed such as UWOT provides25 a detailed picture of the whole system Dishwasher 17.4 0.3 5.2(n) 100%saving 0% (13) 0% on each level (9) response—in terms of requested DW, produced WW and simulated runoff—for a given design Household equipment Tap(s) (n) Regular (39)option thatRegular encompasses (6) all selectedRegular technologies (226) on a household271 and neighborhood scale. This Washing machine (in house) 52.9 0.29 15.3 100% 0%design option 0% could be considered a scenario in the neighborhood, encompassing assumptions on Washing machine Outdoor tap (n) Regular (13) Regular (3) - (0) 16 50 0.25 12.5 100% 0% 0% (common launderette) the mixture, usage and design attributes of technologies, but also implicitly including assumptions Car wash 1 m3/day for the whole neighborhood SewerWaste streams: Sewer on = BWclimate sewage, Sewer (through = theGW use sewage, of rainfall =data In soil to generate runoff) and client behavior (by assuming a set occupancy and frequency of use). Likewise, a neighborhood that features the same spatial and Garden occupancy characteristics but no water-saving or decentralized measures, i.e., a unit of linear water Outdoor tap 50 0.12.3. Formulating 5 the 0%Performance 0% Assessment Chain 100% for Decentralized Urban Water Systems management that requests DW from the central networks and quickly disposes WW and runoff to Using a simulation testbed suchcorresponding as UWOT provides central services, a detailed could picture be considered of the whole another system scenario able to be modeled with UWOT. response—in terms of requested DW, produced WW and simulated runoff—for a given design References It becomes thus evident that UWOT is able to model different ‘realms’ in the form of scenarios that option that encompasses all selectedrepresent technologies management on a household choices, andclimate neighborhood characteristics scale. and This social behaviors through its required design option could be considered a scenario in the neighborhood, encompassing assumptions on 1. Gleick, P.H. Water management: Soft water paths. Nature 2002, 418, 373. [CrossRef][PubMedinputs and] component topology (i.e., house types, RWH and/or GWR treatment unit). A basis for a the mixture, usage and design attributessimulation-based of technologies, assessment but also framework implicitly includingcould then assumptions start with the following conceptualization: “One 2. Muller, M. Adapting to climate change: Water management for urban resilience. Environ. Urban. 2007, on climate (through the use of rainfallcould data assessto generate the performance runoff) and clientof a decentralized behavior (by assumingsystem by a comparingset the ‘decentralized’ scenario 19, 99–113. [CrossRef] occupancy and frequency of use). withLikewise, the currenta neighborhood reality, i.e., that baseline features scenario, the same that spatial reflects and linear management and relies on 3. Baird, G.M. A game plan for aging water infrastructure.occupancyJ.-Am. characteristics Water Works but no Assoc. water-savingcentralized2010, or 102networks.” de,centralized 74–82. measures, i.e., a unit of linear water [CrossRef] management that requests DW from the Thiscentral conceptualization, networks and quickly however, disposes has a caveat: WW and performance runoff to assessment is directly dependent on corresponding central services, could be considered another scenario able to be modeled with UWOT. 4. Moe, C.L.; Rheingans, R.D. Global challenges in water, sanitation and health. J. Waterthe Health output2006 of, the4, 41–57.model, which generates a wealth of data on daily time steps. 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