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ObservationsDYNA ISSN: 0012-7353 Universidad Nacional de Colombia Blanco-Londoño, Sergio Andrés; Torres-Lozada, Patricia; Galvis-Castaño, Alberto Identification of resilience factors, variables and indicators for sustainable management of urban drainage systems DYNA, vol. 84, no. 203, October-December, 2017, pp. 126-133 Universidad Nacional de Colombia DOI: 10.15446/dyna.v84n203.58116 Available in: How to cite Complete issue Scientific Information System Redalyc More information about this article Network of Scientific Journals from Latin America and the Caribbean, Spain and Portugal Journals homepage in Project academic non-profit, developed under the open access initiative Identification of resilience factors, variables and indicators for sustainable management of urban drainage systems • Sergio Andrés Blanco-Londoñoa, Patricia Torres-Lozadaa & Alberto Galvis-Castañob a EIDENAR, Faculty of Engineering, Universidad del Valle, Cali, Colombia., b CINARA Institute, Faculty of Engineering, Universidad del Valle, Cali, Colombia. Received: June 14th, 2016. Received in revised form: July 19th, 2017. Accepted: September 30th, 2017 Abstract Water management systems in general and urban drainage systems (UDS) in particular should be designed to ensure not only the provision of public service but also their sustainability and resilience. This paper performed an analysis and assessment to identify the factors, variables and indicators of resilience for sustainable UDS management by using an information management tool for scientific subjects. As a result of this analysis, four water resource resilience factors were identified: i. Flexibility; ii. Resourcefulness; iii. Redundancy; and iv. Robustness. In addition, six UDS resilience variables were identified: i. Recovery capacity; ii. Response capacity; iii. Amplitude; iv. Absorption capacity; v. Resistance capacity; and vi. Response curve. Corresponding indicators were proposed to quantify these variables. The identified elements contribute to the development of integrated frameworks to assess UDS resilience. Keywords: urban drainage; management; resilience; sustainability. Identificación de factores, variables e indicadores de resiliencia para la gestión sostenible de sistemas de drenaje urbano Resumen La gestión del agua en general y de los sistemas de drenaje urbano (SDU) en particular, debe ser concebida no solo para asegurar la prestación de un servicio público, sino también para garantizar su sostenibilidad y resiliencia. En el presente artículo se presenta un análisis y reflexión que permitió identificar los factores, variables e indicadores de resiliencia para la gestión sostenible de SDU, usando herramientas de gestión de información de temas científicos. Como resultado de este análisis, se identificaron cuatro factores de resiliencia de recursos hídricos: i. Flexibilidad; ii. Recursividad; iii. Redundancia; y iv. Robustez y seis variables de resiliencia de SDU: i. Capacidad de recuperación; ii. Capacidad de respuesta; iii. Amplitud; iv. Capacidad de absorción; v. Capacidad de resistencia; y vi. Curva de respuesta. Para cuantificar estas variables, se proponen sus correspondientes indicadores. Los elementos identificados contribuyen al desarrollo de modelos integrales de evaluación de la resiliencia en SDU. Palabras clave: drenaje urbano; gestión; resiliencia; sostenibilidad. 1. Introduction Cities must be able to face 21st century threats such as rapid urbanization, population growth, climate change and variability, energy restrictions and increased environmental regulation [1]. Novotny [2] noted that urban water management becomes unsustainable in the face of extreme events such as floods or droughts, which are expected to increase in frequency as a result of global warming. Accordingly, there is a need to consider resilience in the planning, design and construction of urban infrastructure as embodied in the Sustainable Development Goals (SDGs) formulated by the UN for 2016-2030 [3]. Urban infrastructure elements such as urban drainage systems (UDS) are vulnerable to these aforementioned threats; therefore, design and operational processes must account for system weaknesses, operational failures, and the incorrect interpretation and use of information to ensure How to cite: Blanco-Londoño, S.A., Torres-Lozada, P. and Galvis-Castaño, A., Identification of resilience factors, variables and indicators for sustainable management of urban drainage systems DYNA, 84(203), pp. 126-133, December, 2017. © The author; licensee Universidad Nacional de Colombia. Revista DYNA, 84(203), pp. 126-133, December, 2017, ISSN 0012-7353 DOI: Blanco-Londoño et al / Revista DYNA, 84(203), pp. 126-133, December, 2017. sustainability [4]. Thus, the concept of resilience comes to the fore when seeking to improve the sustainability of urban infrastructure in the future. Given the importance of this concept in UDS management, we will discuss the evolution of resilience, going from the narrowest sense of the word to a broader interpretation, while examining how it has been addressed as it relates to UDS management. Table 1. Characteristics, perspectives and context of different resilience concepts. Concept 1.1. Evolution of the concept of resilience The concept of resilience arose from the field of ecology in the 1960s through studies of predator-prey population interactions and their functional responses as they relate to the theory of ecological stability. Holling [5] proposed that “resilience determines the persistence of relationships within a system and is a measure of the capacity of these systems to absorb changes in variables of state, variables of conduction and parameters, and being able to persist”. Many years later, Holling [6] classified resilience into “engineering resilience” and “ecological resilience” to emphasize the differences between efficiency, stability and predictability on the one hand and persistence, change and unpredictability on the other. Engineering resilience is characterized by analysing return periods and efficiency by focusing on the recovery capacity of a system in the context of a stable equilibrium. Ecological resilience, in turn, emphasizes the instability that can “move” a system to another regimen of behaviour (also known as the “basin of attraction”). Based on these concepts, Holling [6] defined resilience as “the magnitude of the disturbance that can be absorbed before the system redefines its functional structure, changing the variables and processes that control behaviour”. Resilience is characterized as the capacity to absorb changes, resist disturbances and maintain functions while focusing on the persistence and robustness of a system in the context of global stability. Because these different interpretations of resilience were causing confusion, and by arguing that the resilience of a system must be considered in terms of the attributes that control the dynamics of a system. Walker et al. [7] added a third category: socio-ecological resilience, defined as “the potential of a system to tolerate disturbances without collapsing towards a qualitatively different state, maintaining its structure and function, which involves its capacity to reorganize itself, following the changes driven by disturbances”. Socio-ecological resilience is characterized by analysing the interactions between disturbances, reorganization, sustainability and development in a system and focusing on the capacity to adapt, transform, learn and innovate in a context of unstable equilibrium. This concept modified existing views that considered systems to be stable by introducing a new perspective that considered the capacity of systems to adapt and change, thus increasing the probability of sustainable development in changing environments where the future is unpredictable. Characteristics Perspective Engineering resilience Return period, efficiency Recovery, stability Ecological or social resilience Capacity to absorb, resist shocks, maintain functions Persistence, robustness Socioecological resilience Perturbation and reorganization, sustainability and development Capacity to adapt, transform, learn, innovate Context Surroundings of a stable equilibrium Multiple equilibriums, stability landscapes Integrated feedback of the system, dynamic interactions between scales Source: Adapted from [11] This broad category integrates resilience with socioecologic systems (SES), which integrate social and ecological systems, by focusing not only on the components of both systems but also on their interactions [7]. The SES concept incorporates ideas from fields related to adaptation, robustness and vulnerability by concerning itself with the dynamics and attributes involved in each of these terms, thus becoming broader in scope than any of these individual fields [8,9]. Within the domain of a social system lie subsystems such as culture, politics, the economy, and social organization (society itself); an ecological system domain hosts subsystems such as nature (a setting not created by man) and the environment (a setting created by man) [10]. Folke [11] defined socio-ecological resilience as “an approach or way of thinking that presents a perspective to guide and organize thinking from a broader perspective, providing a valuable framework for the analysis of SES”. This places this field under exploratory research and rapid development, with political implications for sustainable development. Table 1 synthesizes the major characteristics, perspectives and context of the three concepts of resilience we have identified. 1.2. Resilience for sustainable management of UDS Different methodologies have been formulated to quantify the concept of resilience; Hosseini et al. [12] classified the procedure for evaluating resilience into qualitative and quantitative methodologies. The qualitative methodologies include conceptual frameworks (they provide a notion of resilience but do not provide a quantitative value) and semi-quantitative indices (they involve the opinion of experts in their estimation), and the quantitative methodologies include general resilience metrics (they evaluate resilience in the performance of a system) and structural-based models (they evaluate resilience by components). Based on this classification, Table 2 shows several studies that use the different methodologies to evaluate resilience. 127 Blanco-Londoño et al / Revista DYNA, 84(203), pp. 126-133, December, 2017. [13]  [14]  [15]  [16]  [17]  [18]  [19]  [20]  [21]  [22]  [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] Source: Created by author Structural based-models General metrics Semiquantitative Source Conceptual frameworks Table 2. Classification of different methodologies used to evaluate resilience. Qualitative Quantitative             the problem; ii. Search for information; iii. Organization of the information; and iv. Analysis of the information. To define the problem, we began with the key concepts of sustainability, resilience and SES and their relationship with the sustainable management of UDS. The Scopus database was used to search for information by using keywords such as “sustainability”, “resilience”, “socioecological systems” and “urban drainage”, in addition to keywords referring to important problems related to UDS such as “climate change”, “flood risk” and “diffuse pollution”. The period of observation extended from 1979 (when the concept of resilience initially appeared in engineering) until February of 2016. The following search equations were used: “Resilience Field of Study Socio-Ecological Socio-Ecological Urban water Urban water Urban drainage Water resources Socio-Ecological Water supply Water resources Water resources Water resources Water resources Water resources Water resources Water resources Water resources Water resources Water supply Water supply Urban drainage Urban drainage Urban drainage AND Socio-Ecological Systems”, “Resilience AND Sustainability OR Urban Drainage”, “Resilience AND Urban Drainage”, “Resilience AND Sustainability AND Climate Change”, “Resilience AND Flood Risk”, “Socio-Ecological Systems AND Sustainability”, “Socio-Ecological Systems AND Climate Change”, “Sustainability AND Urban Drainage”, “Sustainability AND Climate Change OR Urban Drainage”, “Sustainability AND Diffuse Pollution”, “Sustainability AND Flood Risk”, “Urban Drainage AND Climate Change”, “Urban Drainage AND Diffuse Pollution” and “Urban Drainage AND Flood Risk”. Although different methodologies have been proposed to evaluate resilience, there are few studies that have focused on developing the appropriate methodologies to evaluate resilience in UDS [35,36], which makes the study of resilience in this field a novel topic of research for developing quantifiable indicators of resilience that can evaluate all the aspects involved in this concept [16]. To analyse resilience in UDS, conceptual frameworks [17], general resilience metrics [32,33] and structural-based models [34] have been proposed, the majority of which evaluate flood risk in UDS; only one addresses the problem of dragging pollutants [32]. Based on the conceptual perspective of resilience, these frameworks are primarily framed within the concept of engineering resilience. This provides an opportunity to develop new approaches that can evaluate resilience as it applies to UDS and ideally involve an SES from the point of view of socio-ecological resilience; this topic has not been previously addressed, even though there is a wide field of research to be developed. As a result, this article presents an analysis and related impressions of applying the concept of resilience to the sustainable management of UDS by identifying the various factors, variables and indicators. 2. Methodology The data mining program RefViz [38] was used to organize the information and select the most relevant articles; the program uses mathematical algorithms to group articles by topic based on the keywords. The results were used to create a concept map denominated as a galaxy in which each topic was grouped based on the frequency of the keywords. The organized information yielded 596 articles of interest forming 24 topic groups. Once the information was organized, we selected documents and authors for review. After reading the abstracts and conclusions of those articles, an analysis was performed of the articles with the most important ideas and the most relevant aspects for the topic of this study. Once this was completed, were selected four of the 24 groups formed in the information search stage that were of most interest (groups G16, G17, G19 and G21); these included 122 articles. To make this articles selection, it was taken into account that resilience was a main keyword of the document. The filtered documents were studied in more detail (i.e. it was made a scanning reading of each document) to select those that were relevant for this research (i.e. documents that include all or some factors for the evaluation of resilience in water resources). A total of 19 articles were used to carry out the analysis and comparison of the application of the resilience concept and to identify the common and relevant elements of resilience and the factors, variables and indicators for the sustainable management of UDS. Fig. 1 shows the groups of interest regarding the topics consulted based on the previously described methodology. 3. Results and discussion The methodology for the management of scientific information proposed by Gómez et al. [37] was used to identify the variables and indicators of resilience for the sustainable management of UDS. This methodology consists of four phases: i. Definition of 3.1. Resilience factors in water resource management It was determined that resilience was impacted by four factors: i. Flexibility (capacity to change); ii. Resourcefulness 128 Blanco-Londoño et al / Revista DYNA, 84(203), pp. 126-133, December, 2017. Figure 1. Interest groups in the consulted topics. Period 1979 – February 2016. Source: Created by author Table 3. Characteristics of factors considered to evaluate resilience in the management of water resources. Factor Flexibility Resourcefulness Redundancy Robustness Description Adaptation strategies Capacity to change, evolve and adopt alternative strategies (in the short or long term) in response to changing conditions; implies recognizing when it is not possible to return to the previous condition and searching for new solutions and strategies (evolution) [15,39]. Capacity to visualize and act to identify problems, establish priorities and mobilize resources when faced by conditions that threaten to change an element of the system [15,40]. Flexibility could be increased through the use of spatially distributed systems (decentralized) or modular systems or by providing storage capacity [16,41]. Availability of elements or systems that can be substituted or activated when interruptions occur due to disturbances, allowing vital functions of a system to continue while the redundant elements assume new functions [33,40]. Capacity of systems to resist a particular level of stress without suffering unacceptable degradation or loss of functions [40]. Resourcefulness can be increased by mobilizing human resources and assets (financial, physical, social, environmental and technological resources and information), supporting priorities and establishing goals [15,28]. Redundancy could be improved through the addition of multiple elements or components that provide similar functions to minimize the propagation of failures through the system or operations that make it possible to divert exceptional load conditions to alternative parts of the system [41]. Robustness requires the exploration of the response and recovery of the system over a range of disturbance magnitudes; it also includes analysis of uncertainty caused by the variability of data and randomness of system parameters [42]. Source: Created by author (capacity to mobilize resources); iii. Redundancy (presence of options); and iv. Robustness (capacity to resist) [15,39,40]. Table 3 summarizes the description of each of these factors and their adaptation strategies, whereas Table 4 shows how they have been considered in different areas of water resource management. Flexibility is the most studied factor because this factor is directly associated with the concept of resilience, which has been changing with the incorporation of additional factors. This factor evaluates the capacity to recover and is associated with the time of failure, reliability and recovery speed of a system from the perspective of risk management. Flexibility 129 Blanco-Londoño et al / Revista DYNA, 84(203), pp. 126-133, December, 2017. [14] [17]  [18]  [19] [20]  [21] [22] [23]  [24]  [25]  [26]  [27]  [28]  [29]  [30]  [31]  [32]  [33]  [34]  Factors/Total 15/19 (\%) 79 Source: Created by author   Robustness Redundancy Resourcefulness Source Flexibility Table 4. Factors considered for the evaluation of resilience in different areas of water resource management. Factors           6/19 32 2/19 10 Area of study Socio-Ecological Urban drainage Water resources Socio-Ecological Water supply Water resources Water resources Water resources Water resources Water resources Water resources Water resources Water resources Water resources Water supply Water supply Urban drainage Urban drainage Urban drainage 4/19 21 has been quantified through semi-quantitative indices that measure the capacity to provide a service [20], mathematical functions that quantify the probability of system failure [2327,29,32], system reliability [30,31] and indicators based on performance curves of a system that provide information on its behaviour before and after a disturbance [17,18,28,33,34]. Resourcefulness evaluates the availability of economic and social resources and is associated with the capacity to mobilize these resources in adverse conditions. This factor has been quantified through economic variables based on the estimation of damage caused to people and infrastructure [17,18,34], as well as social variables based on economic development, demographic trends, political stability, government policies, market incentives, media organization [14], resource diversity, community and institutional learning, system self-organization [19], governance [20], communication, risk perception, interaction between institutions and risk management policies and tools [22]. Redundancy evaluates the multiplicity of elements that allow the vital functions of a system to continue and is associated with the availability of redundant elements that carry out these functions. This factor has been quantified through metrics such as grouping and meshing coefficients [31], along with an index that combines the magnitude and duration of system failures where redundancy is evaluated by comparing how an existing system functions with and without redundant elements until failure occurs [33]. Of the four identified factors, this is the least considered because of its recent incorporation, which is primarily related to the evaluation of resilience in urban water systems [16,41]. Table 5. Variables and indicators used to evaluate the resilience of UDS. Factors Variables Indicators Source Index of failure [23-27,29] Gradualness [17,18] Duration of recovery [17] Capacity to Recovery rate [18] Flexibility recover Loss of recovery [28] Environmental load [32] capacity Recovery indicator [33,34] Response Response indicator [34] capacity Damage expected per [17,18] Resourcefulness year Amplitude Expected number of affected individuals [18] per year Capacity of Severity [33] Redundancy absorption Resistance Overload of the [17] capacity system Resistance threshold [21] Severity of the Robustness [21] Response response curve Proportionality of the [21] response Point of no recovery [21] Source: Created by author Robustness evaluates the resistance of a system when faced with extreme or unexpected events and is associated with systems that function well, even under uncertain conditions. This factor has been quantified by analysing the resistance capacity of a system by estimating an overload [17] through a variable that addresses change and uncertainty [19]. This is based on a graph that describes the level at which one can establish how a system responds to different levels of disturbance [21] and through metrics such as the central dominance point, the density of articulation points, the density of joints, the spectral void and algebraic connectivity [31]. 3.2. Variables and indicators of resilience for sustainable management of UDS Based on the analysis of these factors, a group of variables and indicators was identified to evaluate the resilience of UDS; these are summarized in Table 5. Flexibility is associated with the variable of recovery capacity, which evaluates the possibility of a drainage system to return to a normal or stable state after a disturbance; this variable includes several indicators and is the variable most studied by researchers. The indicators proposed for this variable are the failure index, which quantifies the probability of system failure [2327,29]; the gradualness, which measures the change in the response of a system with respect to the change of magnitude in a flood surge [17,18]; the recovery duration, which quantifies the time it takes for a system to recover from an unsatisfactory condition [17]; the recovery rate, which measures the recovery rate of the system after a flood [18]; the recovery loss, which quantifies the loss of quality in a 130 Blanco-Londoño et al / Revista DYNA, 84(203), pp. 126-133, December, 2017. system [28]; the environmental load capacity, which quantifies the amount of pollutant emissions that a system can endure [32]; and the recovery indicator, which measures the recovery time from a flood at each node of the system [33,34]. Resourcefulness is associated with two variables. One variable is the response capacity, which evaluates how the components of a drainage system respond to disturbances through a response indicator that allows for estimating the response magnitude in the area surrounding a flooded node [34]. The other variable is the amplitude [35], which evaluates the severity of damage expected in a drainage system after a disturbance through a yearly damage indicator that measures the average damage costs [17,18] and the expected number of affected individuals in a given year [18]. Redundancy is associated with the variable of absorption capacity [43], which evaluates the alternatives that can be offered by a drainage system after the failure of one or more of its component; the key indicator is severity, which measures the magnitude and duration of the maximum failure [33]. Robustness is associated with two variables. One variable is the resistance capacity, which evaluates the magnitude of the damage that a drainage system can endure through a system overload indicator that measures the greatest precipitation intensity that a system can endure [17]. The other variable is the response curve [21], which represents the aspects of robustness applicable to a UDS. The curve shows how a system responds to different disturbance levels through indicators such as the resistance threshold (which measures the point at which the response becomes greater than zero), the severity of the response (which corresponds to the point at which a system is no longer in a normal situation), the proportionality of the response (which relates the response change to the magnitude of the disturbance) and the point of no recovery (which is the point at which a system changes its identity into a new configuration). Although this study identified resilience factors, variables and indicators in the sustainable management of UDS, continued research exploring other information sources (e.g., other databases besides Scopus, theses, government documents, etc.) is necessary to identify additional elements. In addition, it will also be necessary to develop more comprehensive conceptual frameworks that can consider all of these factors, variables and indicators and validate them through case studies. However, combination of these factors and what could be the result of this combination is a pending work to be done. The elements identified in this study can serve as a first step in the development of these comprehensive frameworks. 4. Conclusions The concept of resilience has evolved over the past 40 years, with a diversity of concepts arising from a narrow perspective with specific applications (engineering resilience) to a broader perspective that encompasses a more integral application context (socio-ecological resilience). The concept of resilience has been scarcely studied as it pertains to UDS management, and the primary application has been in the study of flood risk. Therefore, much remains to be developed in this key field of research. This study identified four key factors for the evaluation of resilience in water resources: flexibility, resourcefulness, redundancy and robustness. These factors evaluate the capacity to change, the mobilization of resources, the presence of options and the resistance capacity, respectively. The following variables were identified to analyse resilience in UDS: recovery capacity, response capacity, amplitude, absorption capacity, resistance capacity and response curve. 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Gómez-Luna, E., Navas, D., Aponte-Mayor, G. y BetancourtBuitrago, L., Metodología para la revisión bibliográfica y la gestión de información de temas científicos, a través de su estructuración y sistematización. DYNA [Online]. 81(184), 2014. [date of reference March 2016]. Available at: articulo.oa?id=49630405022 Agrawal, A., Using RefViz© with EndNote [Online]. Springer Science + Business Media, 2006 [date of reference March 2016], cap. 12, Endnote 1-2-3 Easy!: Reference Management for the Professional. Available at:\%2F978-0-387-95901-6_12 Bruneau, M. and Reinhorn, A., Exploring the concept of seismic resilience for acute care facilities. Earthquake Spectra, 23(1), pp. 4162, 2007. DOI: 10.1193/1.2431396 Pandit, A., Resilience of urban water systems: An Infrastructure Ecology approach to Sustainable and Resilient (SuRe) planning and design, Ph.D. Thesis, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, USA, 2014. Mugume, S., Diao, K., Astaraie-Imani, M., Fu, G., Farmani, R. and Butler, D., Enhancing resilience in urban water systems for future cities. Water Science and Technology: Water Supply, 15(6), pp. 13431352, 2015. DOI: 10.2166/ws.2015.098 Anderies, J., Folke, C., Walker, B. and Ostrom, E., Aligning key concepts for global change policy: Robustness, resilience, and sustainability. Ecology and Society, 18(2), 8, 2013. DOI: 10.5751/ES05178-180208 Balsells, M., Barroca, B., Amdal, J., Diab, Y., Becue V. and Serre, D., Analysing urban resilience through alternative stormwater management options: Application of the conceptual Spatial Decision Support System model at the neighbourhood scale. Water Science & Technology, 68(11), pp. 2448-2457, 2013. DOI: 10.2166/wst.2013.527 S.A. Blanco-Londoño, received the BSc. Eng in Civil Engineering in 2008, and the MSc degree in Water Resources in 2014. He is currently a PhD student in Sanitary and Environmental Engineering at the Universidad del Valle - Cali, Colombia. His research interests include hydraulic and hydrological modelling, water sanitation, sustainability and resilience. ORCID: 0000-0002-2048-268X 132 Blanco-Londoño et al / Revista DYNA, 84(203), pp. 126-133, December, 2017. P. Torres-Lozada, received the BSc. Eng in Sanitary Engineering in 1988, the MSc. degree in Civil Engineering: Hydraulics and Sanitation in 1992, and the PhD degree in Civil Engineering: Hydraulics and Sanitation in 2001. She is currently a titular professor of the Faculty of Engineering of the Universidad del Valle - Cali, Colombia and a senior researcher in call for COLCIENCIAS-2016. Her research interests include water quality and treatment, treatment of wastewater, solid waste and soil. ORCID: 0000-0001-9323-6677 A. Galvis-Castaño, received the BSc. Eng in Sanitary Engineering in 1979, and MSc. degree in Industrial and Systems Engineering in 1998. He is currently a titular professor of the Faculty of Engineering of the Universidad del Valle - Cali, Colombia. His research interests include technology selection in water and sanitation and mathematical modelling applied to the planning and management of water resources. ORCID: 0000-0002-4158-1919 Área Curricular de Medio Ambiente Oferta de Posgrados Especialización en Aprovechamiento de Recursos Hidráulicos Especialización en Gestión Ambiental Maestría en Ingeniería Recursos Hidráulicos Maestría en Medio Ambiente y Desarrollo Doctorado en Ingeniería - Recursos Hidráulicos Doctorado Interinstitucional en Ciencias del Mar Mayor información: E-mail: Teléfono: (57-4) 425 5105 133 Land Use Policy 89 (2019) 104251 Contents lists available at ScienceDirect Land Use Policy journal homepage: Bringing community perceptions into sustainable urban drainage systems: The experience of Extremadura, Spain T ⁎ L.A. Sañudo-Fontanedaa,b, , Rafael Robina-Ramírezc a INDUROT Research Institute, UOStormwater, GICONSIME Research Group, Department of Construction and Manufacturing Engineering, University of Oviedo. Polytechnic School of Mieres. Calle Gonzalo Gutierrez Quiros s/n. 33600, Mieres (Principality of Asturias), Spain b Centre for Agroecology, Water and Resilience (CAWR), Coventry University, Ryton Gardens, Wolston Lane, Ryton-on-Dunsmore, CV8 3LG, Coventry, UK c University of Extremadura, Avenida de la Universidad s/n, 10071, Cáceres (Extremadura), Spain A R T I C LE I N FO A B S T R A C T Keywords: Amenity Community resilience Food and water systems Green stormwater infrastructure Self-organisation Water sensitive urban design Sustainable Drainage Systems (SuDS) have arisen as an alternative to “grey” conventional drainage in order to manage stormwater in urbanised areas. While technical aspects regarding the design and construction of SuDS have received most of the attention by academics and practitioners across the world, social aspects such as amenity, health, governance or equity, amongst others, still are not fully considered for design, planning and operation. The present research introduces human aspects of water management beyond traditional schemes to examine community perceptions about SuDS. With this aim, the Smart PLS Path Modelling method has been designed to measure social unobserved variables through indicators, using the UNESCO’s principles. A case study was developed at three neighbouring communities in Cáceres (region of Extremadura), Spain, in order to check the potential of SuDS to be considered for full implementation in Southern Europe. A questionnaire was designed and conducted using 276 dwellers whose average was 39. The participants showed significant sensitivity towards the implementation of SUDS. This research opens a new research line by tackling the knowledge gap identified, informing on how to approach young communities with few or no knowledge about SuDS. 1. Introduction Food and water systems are under threat due to instability processes governed by climate change, biodiversity loss and intense urbanisation, affecting community resilience across the globe (Altieri et al., 2015). Flood events, water pollution and large periods of droughts are increasingly dominating planning scenarios for cities whilst inducing insecurity both in food and water systems, not only in urban environments but also in rural areas (Nguyen et al., 2019). Extreme values within design parameters have changed drastically in many cases (Stephens et al., 2018), leading the path towards newer techniques and knowledge to sustainably manage water under scenarios of climate change and large waterproofed urbanised areas (Allende-Prieto et al., 2018). There is a wide agreement amongst scientists and practitioners in pointing out Sustainable Drainage Systems (SuDS) as the most complete set of techniques to provide resilient water systems for practice under the “new paradigm for water management” which confers value to rainwater in comparison to conventional drainage systems (Morison and Brown, 2011; Morison and Chesterfield, 2012; Perales-Momparler et al., 2017; Rodríguez-Rojas et al., 2017). Despite the fact that this paradigm was key in Ancient Civilisations as shown in Charlesworth et al., 2016, the driving factor in drainage has been to focus on taking rainwater away from the urban environment considering it as waste. SuDS design comprehends four main pillars according to the UK CIRIA SuDS Manual (Woods Ballard 2015): water quantity, water quality, biodiversity and amenity. SuDS philosophy often referred as Water Sensitive Urban Design (WSUD) (Fletcher et al., 2014) shows a wide range of benefits from SuDS implementation, highlighting Ecosystem Services amongst others. Furthermore, an ecohydrological approach could comprehend multiple benefits comprising flood mitigation, water supply, thermal comfort, and social amenity using the natural flow paradigm (Fletcher et al., 2014). Linking ecosystem services from Green Stormwater Infrastructure (GSI) to human well-being requires a multidisciplinary approach where planners have to follow very often a route from multifunctionality towards multiple ecosystem services (Hansen and Pauleit, 2014). Thus, the socio-cultural context or human well-being should be linked to the ecosystem and biodiversity. In addition, human health is directly related to the promotion of ⁎ Corresponding author at: INDUROT Research Institute, UOStormwater, GICONSIME Research Group, Department of Construction and Manufacturing Engineering, University of Oviedo. Polytechnic School of Mieres. Calle Gonzalo Gutierrez Quiros s/n., 33600, Mieres (Principality of Asturias), Spain. E-mail addresses:, (L.A. Sañudo-Fontaneda), (R. Robina-Ramírez). Received 7 December 2018; Received in revised form 23 August 2019; Accepted 20 September 2019 Available online 26 September 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved. Land Use Policy 89 (2019) 104251 L.A. Sañudo-Fontaneda and R. Robina-Ramírez principles refer to a set of water related ethics and values, which help achieving sustainable water management: human dignity and the right to water, equity, vicinity, frugality, transaction, multiple and beneficial use of water, mandatory application of water quality and quantity measures, compensation and user pays, polluter pays, participation, and equitable and reasonable utilization. The authors found a positive impact on the “Principles of water governance” and the “Water principles”, showing the path for further research in what has been called as “the new paradigm in water management” chiefly sustained by the application of the WSUD philosophy and the design and implementation of SuDS techniques. Nevertheless, regions such as Southern Europe lack generally of standards and laws that empower the use of SuDS at a national and/or regional level (Andrés-Valeri et al., 2016), representing an interesting case study to test new methods which include human aspects at core. Spain represents the case for a developed country where SuDS are not fully developed yet despite the fact that multiple researches have been conducted over the last 20 years (Castro-Fresno et al., 2013). Furthermore, Spanish climate offers multiple challenges due to its wide variety from low rainfall regimes, including desert areas in the South, up to high annual rainfall volumes in the North (AEMET, 2018). The role of communities in defining water sensitive strategies to overcome water-related problems has increased drastically over the last years (Wong and Brown, 2009). However, it still is an underdeveloped area in countries like Spain and other countries in the wider Southern Europe region. It is important to note that SuDS implementation has proven to be effective from a technical point of view in Mediterranean regions of Spain (Perales-Momparler et al., 2015) and other climates within the country (Castro-Fresno et al., 2013; Andrés-Valeri et al., 2016), leading the path to further implementation over the last 5 years. This article targets three neighbouring communities of dwellers in Cáceres (region of Extremadura), Spain (Fig. 1), where the average annual rainfall is 518 mm, corresponding to a Csa in the Köppen-Geiger climatic classification (Essenwanger, 2001). This case is representative for larger parts of South Spain and the Mediterranean region in Southern Europe. This research also introduces a novel approach to communities of young dwellers whose average age was 39 for our case study, and how they are willing to uptake new approaches to water management based on cultural ecosystem services which empowered social interactions as stated by Riechers et al. 2018. The application of Ramírez and Sañudo-Fontaneda´s (2018) approach, based on the Structural Equation Modelling using variance (SEM) and the PLS, was especially tailored-made for this research embodying human aspects. The methodology contains a transformative potential for change, related to community self-organisation (Bos and Brown, 2012), where an informed community of dwellers could implement SuDS at a stakeholder level, leading the way for resilience in water systems within buildings and their surrounding areas. Therefore, these initial experiences working with communities at these targeted areas with potential for SuDS development in Southern Europe could inform policies which enable the wider design, practices, planning and operation. With this main aim, this research was set under two main objectives: ecosystem services by using GSI (Tzoulas et al., 2007). Thus, ecosystem services have been investigated before in relation with human aspects. Following this route, Lundy and Wade (2011) described cultural services as part of a category of ecosystem services which provides spiritual and educational values, aesthetics and recreation. These human aspects from the ecosystem services associated with GSI impacted positively in mental and physical well-being, increased environmental awareness and house prices (Lundy and Wade, 2011). Kong et al. (2007) also linked amenity values to market prices. Age is also a factor that influences environmental awareness and the interaction with nature (McKeiver and Gadenne, 2005; Kanchanapibul et al., 2014) and should be taken into consideration when undertaking amenity surveys in SuDS as an environmental solution. Moreover, Wong and Brown (2009) defined three pillars of practice for water sensitive cities based upon cities as water supply catchments, cities providing ecosystem services and cities comprising water sensitive communities. The later could be considered as the recipient for human aspects and behaviours, being the other two pillars those related to infrastructure and built and natural environments. Given the complex nature of the problem and the multifunctional scale offered by “the new paradigm for water management”, there is a need to link natural, social and environmental systems, and the role of communities around them in increasing resilience to change (Morison and Chesterfield, 2012). Community self-organisation plays a key role through adaptation processes which should be led by information and understanding schemes about the techniques available and the potential implementation at their specific locations (Djalante et al., 2013; Atkinson et al., 2017). Following up from this reasoning, Bos and Brown (2012) highlighted that SUDS technologies should be socially embedded in order to create a path towards successful implementation in practice. Previous researches have showed a socio-technical transition for the implementation of the WSUD philosophy where community-based research has been proved a key tool to produce resilient practices under climate change scenarios (Visconti 2017). Wong and Brown (2009) and Ferguson et al. (2013) also identified that the socioinstitutional dimension of WSUD was a major area of research, which needed further development as it is key for SuDS implementation. In consequence, human aspects have been merely considered through the amenity concept of SuDS, being defined as “a useful or pleasant facility or service” by Woods Ballard et al. (2015). This concept for amenity comprehends urban design or space quality, liveability or quality of life for inhabitants, and aesthetic appreciation amongst others. Furthermore, Fletcher et al. (2015) mentioned amenity as the second point within the WSUD objectives, being commonly associated with habitat/biodiversity as per pointed out by Woods Ballard et al. (2015). Based upon the need to incorporate human aspects to water related problems, Ramírez et al. (2016) proposed a new approach to water management by considering human aspects and their impact in the implementation of best water management practices in Mexico. Further research was carried out in South Africa, challenging the Smart Partial Least Squares (PLS) method for impoverished settlements, showing that water services can benefit from considering human aspects in their planning (Ramírez and Sañudo-Fontaneda, 2018). PLS represents a powerful and effective means to test multivariate structural models with latent variables. The primary purpose of the PLS approach is to predict the indicators by means of the components expansion (Jöreskog and Wold, 1982). In line with this notion, Hair et al. (2011) recommend using PLS if the goal is predicting key target constructs or identifying key driver constructs. The authors used an application of the wellknown technology acceptance model estimation which uses a dataset called Smart PLS (Ringle et al., 2015). Ramírez and Sañudo-Fontaneda’s research introduced principles of “human dignity” and “human equality”, travelling beyond traditional schemes of water management, in order to envisage water policies to provide basic water services, using as a framework the UNESCO’s principles (UNESCO, 2011). UNESCO’s 1 To demonstrate that the combination of the SEM and PLS methods can sustain the development of an integral approach to value community perceptions for SuDS practice. 2 To check whether communities of young-aged people present significant sensitivity towards SuDS when setting up environmental, ethical and Nature-Based Solutions (NBS). 2. Methods 2.1. Experimental design and hypotheses for the study Hypotheses 2 for this research were designed focusing in Land Use Policy 89 (2019) 104251 L.A. Sañudo-Fontaneda and R. Robina-Ramírez Fig. 1. Neighbouring communities of dwellers participating in the study (highlighted in yellow), and surrounding areas (Source: Adapted from Google Maps) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). Based on Chin´s definition of Latent variables (Chin, 1998), the purpose of the present research is to turn the not directly observed variables or constructs into observable items that can be analysed. This allows getting the members of the community’s opinion in order to build the SEM model. Therefore, conceptualizing each latent variable, and then, building the items based on the literature review. The model showed in Fig. 2 is centred in community perceptions for practice under the change in the water management paradigm. With this aim, SuDS are tested under two main premises: firstly, to define the degree of importance given by the dwellers to stormwater management under climate change scenario; and secondly, as to how willing communities are to implement SuDS through a process of information focused on the multiple benefits provided by them. Therefore, four main latent variables were selected using the previously cited four pillars of SuDS (Fig. 2). understanding how the local communities of dwellers were openminded or not to uptake SuDS for implementation in their buildings and surrounding urbanised areas by being informed about the benefits provided by them in line with improving liveability conditions. An integrated approach based on the four pillars of SuDS (Woods Ballard et al., 2015) was taken, testing the following latent variables, which are underlying variables that cannot be observed directly, also known as constructs or factors as explained by Chin (1998): “Environmental Benefit for the Ecosystem” (EBE), the “Environmental Transformation in Urban Areas” (ET), the “SuDS methods” (SuDS), and the “Amenities Benefit for the Community” (ABC) (Fig. 2); under the following hypotheses: •H •H •H •H •H 1 2 3 4 5 – – – – – SuDS positively influence EBE. SuDS positively influence ET. ET positively influences EBE. SuDS positively influence ABC. ABC positively influences ET. 3 Land Use Policy 89 (2019) 104251 L.A. Sañudo-Fontaneda and R. Robina-Ramírez Fig. 2. Human Aspects of SuDS: a model to value community perceptions for practice considering the 4 pillars of SuDS. Table 1 Original indicators and questions. Original indicators Questions EBE1: Environmental: SUDS provide secure surface water management Is important for you to have an adequate system to control, catch, infiltrate, store and reuse water? Do you consider as an important matter the investment to avoid the deterioration of the drainage system in order to save potable water? Do you give importance to have new drainage systems available beyond conventional drainage which adapt better to climate changes scenarios including extreme temperatures and rainfall events? How would you value drainage systems based upon the improvement of green areas like gardens and ponds, providing more attractive places for the neighbourhood? Do you account as a key factor the planning to implement drainage solutions such as permeable pavements and bioretention in order to improve to the existing drainage systems? Is it important for you to reduce overflows, flooding issues and the negative effects of stagnant water by providing solutions applied at source level. Do you think that SuDS techniques could be implemented in your residence area making it more attractive visually and integrated in the larger urban area? Do you believe that SuDS techniques are robust and safe solutions to manage rainfall and runoff water, reducing flooding issues whilst saving potable water? Do you think that SuDS favor áreas such as recreation, socio-educative, health, tourism and aesthetics? Do you perceive barriers for the implementation of SuDS in your residential areas (i.e. legal, technical, organisational, economical, planning based barriers, etc.) Do you believe that SuDS could improve ecological consciousness in residential areas as well as in education centres? Do you consider important the implementation of SuDS applied to buildings like green roofs in order to control problems derived from intense rainfall at a building level? First flush effect produces significant pollutant risks in urban environments. Do you perceive as an important issue the option to have drainage systems able to reduce these pollution effects? Do you perceive SuDS as tools that help in creating greener spaces which contributes to the improvement of liveability conditions? Do you think that SuDS promote biodiversity in urban environments? EBE2: Socio-economic: increase in investment in comparison to conventional drainage systems, water saving, socio-economic value. EBE3: Develop resilience/adaptability to future change: SUDS designed considering climate change, SUDS contributing to climate resilience, SUDS impact for community resilience and adaptation. ET1: planting and vegetation such as bioretention areas, wetlands, ponds and raingardens, creating attractive landscapes ET2: engineered and robust solutions such as permeable pavements ET3: treat water close to the point where it falls, avoiding combined sewer overflows, flooding issues and ponding effects in the streets ABP1: Enhance visual character/historical: integration in the surrounding area, SUDS designed to be visually attractive, level of support of local heritage and landscape. ABP2: Improve security/safety: security perception in the public, impact on safety measures, prevention. ABP3: Maximise multi-functionality: number of uses/functions, quality of multifunctional uses, ecosystem services. ABP4: Legal: local regulations, legal barriers, national and international contexts. ABP5: Community learning/education: community awareness, school involvement, education strategies. SUDS1: runoff quantity control SUDS2: runoff quality management to prevent pollution SUDS3: create and sustain better spaces for people to live SUDS4: create and sustain better spaces for nature bringing biodiversity back to the city 4 Land Use Policy 89 (2019) 104251 L.A. Sañudo-Fontaneda and R. Robina-Ramírez ponds in urban environments The second meeting was organised with the focus set in discussing the way in which the items turned into questions to be formulated through focus groups organised in October 2018 (Table 1). A pre-test was conducted according to the questions proposed in this meeting. Then, ten households were randomly selected to validate the questionnaire. Eventually, four out of fifteen questions were improved accordingly as seen in Table 1. Additionally, twenty questionnaires were not completed appropriately, being removed from the study. The data were analysed through Smart PLS Path Modelling. This method is conveniently used when the data are interdependent one to another within the constructs and the indicators. Those observables variables measure the latent variables (Sarstedt et al., 2016). For an initial assessment of PLS-SEM model, some basic elements should be covered in the research report. If a reflective measurement model is used, which is the case for this study, the following topics have to be discussed: indicator reliability, internal consistency reliability, convergent validity, discriminant validity, checking structural path, and significance in bootstrapping. Smart PLS presents path modelling estimations not only in the Modelling Window but also in a text-based report which is accessible via the “Report” menu (Ringle et al., 2015). The PLS method was also applied, having been reported to be recommended for use in composite constructs (Rigdon et al., 2017). PLSSEM allows estimating latent variables that represent different model types such as composite models. Those composite can be ‘Mode A’ in case of reflective measurement, which is the case of this research (i.e., the outer weights are the correlations between the construct and the indicators). 2.2. Questionnaire and area of study The indicators drafted for this research (Table 1) were constructed based on an extensive literature review carried out prior to this stage. Several meetings were organized with the objective to explain the scientific aims of the study as well as the hypotheses. The aim for the first meeting was to present all information to the Municipality´s Urban Department and the managers of the residential areas targeted for this research (three neighbouring communities as it can be seen in the three buildings highlighted in Fig. 1). Then, four meetings were organised to collect the data (two of them were celebrated at the Cáceres City Council House and the remaining two at the neighbouring Association´s office). The meetings were organised each two weeks within a period of two months between October and November 2018. The attendees were the Urban Service´s Manager Director, two Engineers and one Biologist from the Maintenance Service of the City Council, and the Neighbouring Association´s Manager Director and two Workers which run the public services between the neighbourhood and the City Council. Finally, three neighbours who are responsible to deal with the Neighbouring Association were also involved. Therefore, ten professionals were actively involved in those meetings. 276 neighbours out of a total of 288 from this residential area (12 non-valid questionnaires were excluded due to some not answered questions), constructed in 2005, participated in the study, presenting an average age of 39 years old. The demographic characteristics of the participants are shown in Table 2. The studied area was especially selected due to this low average age; likewise, the interaction with the environment has been reported to be strong in previous studies (McKeiver and Gadenne, 2005; Kanchanapibul et al., 2014). The neighbourhood is surrounded by two parks whilst a lake is located in the central area (Fig. 1). Families spend long time during the weekend on the green areas due to its appropriate facilities and their recreational value, showing already one of the most characteristic social ecosystem services provided by lakes, wetlands and 3. Data analyses 3.1. Analyses of the measurement model The individual reliability was measured in first place. Table 3 shows the load (λ) of each item, being basically applied at a level of acceptance for the items. Values were higher than λ > = 0.707 (Carmines and Zeller, 1979). Reliability and convergent consistency of each construct were assessed. Firstly, two indicators were used to test the consistency of the construct based on Götz et al., 2010: Cronbachs alpha and its Composite Reliability (CR). Those indicators (Cronbachs alpha and its Composite Reliability) evaluates the rigour with which each indicator measures their correspondent latent variable. The limit of acceptance for each construct is generally established between 0.6 and 0.7 for both Table 2 Main characteristics of the participants. Information Gender Male Female Age 25 years or younger 26-35 years old 36-45 years old 46-55 years old 56-65 years old 60 years old and above Type of family Live alone Family without children Family with two or less children Family with three or more children Education Primary School Secundary school Bachelor University Family incomes (per year) Less than 10,000€ 10,000-15,000€ 15,001€-20,000€ 20,001€-30,000€ 30,001€-50,000€ Higher than 50,000€ N = 242 Percentage (\%) 132 110 242 55\% 45\% 100\% 52 92 39 29 16 14 242 21\% 38\% 16\% 12\% 7\% 11\% 100\% 32 42 122 46 242 13\% 17\% 50\% 19\% 100\% 10 32 80 120 242 4\% 13\% 33\% 50\% 100\% 8 10 42 118 52 12 242 3\% 4\% 17\% 49\% 21\% 5\% 100\% Table 3 Individual reliability, Cronbach Alpha, rho_A, Composite Reliability and Average Variance Extracted (AVE). Latent variables Indicator EBE EBE1 EBE2 EBE3 ET1 ET2 ET3 ABC1 ABC2 ABC3 ABC4 ABC5 SuDS1 SuDS2 SuDS3 SuDS4 ET ABC SuDS 5 Loadings 0.827 0.819 0.765 0.706 0.719 0.701 0.754 0.784 0.701 0.754 0.931 0.775 0.755 0.769 0.866 Cronbachs Alpha rho_A Composite Reliability 0.854 0.855 0.853 0.659 0.752 0.752 0.502 0.891 0.898 0.891 0.622 0.871 0.874 0.871 0.628 0.751 Average Variance Extracted (AVE) Land Use Policy 89 (2019) 104251 L.A. Sañudo-Fontaneda and R. Robina-Ramírez expressed in Table 5, all relationships were significant at 99.9\% confidence level, except for the relationship between ABC and EBE (β = 0.269, p-value = 3.503) and SuDS and EBE (β = 0.205, pvalue = 0.027). Whereas the first one was supported by a 99\% of confidence interval the second one was alternatively supported at 95\%. The relationships which presented the highest load values were SuDS and ET (β = 0.710, T-Statistic = 11.702) and SuDS and ABC (β = 0.600, Statistical T = 10.914). The blindfolding measures the level of prediction within the established model. In this regard, several data from the construct were be used as the estimation parameters in order to estimate the predictive capacity following Chin (1998). The application of Stone-Geisser’s test (Q²) (Stone, 1974; Geisser, 1974) allowed the analysis of the prediction capacity, revealing that the fixed model is predictive (Q2 = 0.437) since Q2 > 0. Table 4 Measurement Model: Discriminant validity. Heterotrait-monotrait ratio (HTMT) ABC EBE ET SuDS ABC EBE ET 0.721 0.604 0.596 0.830 0.736 0.710 SuDS the Cronbachs alpha and the CR (Hair et al., 2005). As it can be seen in Table 3, all the results ranged between those limits for minimum validity. Moreover, another indicator is tested (the rho_A) based on Dijkstra and Henseler (2015). It was also verified in all constructs which values exceeded 0.7. Secondly, the Average Variance Extracted (AVE) was used in order to measure the convergent validity in PLS-SEM. The value of this indicator should be higher than 0.5 to be accepted. Table 3 shows that all constructs met this criterion. Henseler et al. (2015) found the lack of studies to appropriately justify the discriminant validity. Therefore, they addressed a new technique known as the heterotrait-monotrait ratio (HTMT). The results obtained from the current research by applying this method have been listed in Table 4, showing that the assessed model is satisfactory. Thus, the HTMT ratio presented values lower than 0.9 (Gold et al., 2001). the Standardized Root Mean square Residual (SRMR) was utilised in order to analyse the adjustment of the model. This indicator indicates the correlation matrix implied in the model and the observed correlation matrix. In the studied case, SRMR value was 0.073 which is lower than 0.08 which is the upper limit established by Hu and Bentler (1998), therefore providing good fit. 4. Discussions 4.1. Theoretical implications This research studied the perception of SuDS among neighbouring communities in a residential area located in Cáceres. Theoretical implications can be drawn from the results obtained, adding new findings to the general knowledge gap identified in the literature about the perception of SuDS in residential communities in Southern Europe. These findings from this research unfold that neighbours gave special consideration to SuDS under a new scenario for stormwater management derived from the new paradigm of water management. This importance was significantly manifested by the fact that the relations showing higher statistical load were achieved in H2= SuDS → ET (β = 0.710, T-Statistic = 11.702). This also translates into the fact that SuDS has a strong potential to environmentally transform urban areas. Similarly, SuDS are perceived by the community as providers of amenities and benefits for communities as per indicated by SuDS → ABC (β = 0.600, Statistical T = 10.914). Both hypotheses were accepted under a 99\% confidence level. Hence, from the theoretical point of view, this research conveys that the application of SuDS has an important effect not only for the communities but also for the urban environment, as it was strongly perceived by the community studied in this case study. In addition, H3= ET - > EBE (β = 0.526, Statistical T = 4.046) and H3= ABC- > EBE (β = 0.269, Statistical T = 3.053) were found to be highly significant. This means that both the environmental transformation in urban areas, as well as its benefit for the community and amenities, impact positively in the ecosystem as perceived by the social fabric. Nevertheless, the direct effect of SuDS over the environmental benefit for the ecosystem has the lowest significant level (95\% interval confidence), nevertheless being high and significant in any case. This implication can be explained due to the novelty of SuDS and by the fact that they had not been appropriately understood by the community prior to this research. Therefore, further guidance and information are needed in order to improve understanding of SuDS techniques within the community supported by what it was reported by Bastien et al. (2012). Moreover, the barriers were identified as organisational such as lack of information about procedures, legal (i.e. uncertainty of the normatives to apply SuDS as per indicated by Williams et al., 2019), technical (uncertainty about the systems performance), planning (coordination of the steps to carry out the method and its relation to future problems), and economic such as the cost of maintenance. 3.2. Structural model analyses The structural model analysed the hypotheses formulated in 2.1. The analytical significance of the path coefficients was calculated using the Bootstrapp technic based on a 5000-sample (Tenenhaus et al., 2005). According to Chin (1998) the coefficient of determination (R2) evaluates the structural model. In consequence, Chin (1998) reported that R2 values ranging from 0.67 down to 0.33 and 0.19 can be considered strong, moderate and weak, respectively. Our internal latent variable provided moderate values (ABC’s R2 = 0.360, ET’s R2 = 0.505). The main endogenous construct yielded strong values (EBE’s R2 = 0.783). AS a result of these findings, it is concluded that the results convey the applicability of the model within SuDS. Therefore, meaning that EBE has a high explanatory capacity through the remaining two latent variables ABC and ET. In addition, Table 5 showed that the results reached in this study supported all relationships. Then, and according to the results Table 5 Comparison of Hypotheses. Hypotheses Effect Path coeff (β) t-statistic (β/ STDEV) pValue Supported H1 SuDS - > EBE SuDS - > ET ET - > EBE SuDS - > ABC ABC - > EBE 0.205 1.927 0.027 Yes * 0.710 0.526 0.600 11.702 4.046 10.914 0.000 0.000 0.000 Yes *** Yes *** Yes *** 0.269 3.053 0.001 Yes ** H2 H3 H4 H5 4.2. Practical implications Notes: For n = 5000 subsamples, for t-distribution (499) Student´s in single queue: * p < 0.05 (t(0.05;499) = 1.64791345); ** p < 0.01 (t (0.01;499) = 2.333843952); *** p < 0.001 (t(0.001;499) = 3.106644601), n.s. : not significant. SuDS not only influenced the improvement of the ecosystems through an environmental transformation in urban areas at an empirical level, but also through its benefits for the communities and 6 Land Use Policy 89 (2019) 104251 L.A. Sañudo-Fontaneda and R. Robina-Ramírez amenities as it has been demonstrated by this study. Communities are aware of the potential benefit for the urban environment and its functional uses for them through consultation and participation in the process developed in this research. In consequence, communities understood that SuDS contributes towards protecting nature, prioritising environmental matters and help to develop consciousness of the potential environmental damage that the current conventional drainage systems have been contributing to develop under climate change scenarios. Finally, communities showed a significant sensitivity towards SuDS by setting up environmental and ethical solutions. This reasoning meaning that the community studied in this research was willing to consider environmental solutions related to ecosystem services through the design and implementation of SuDS. Furthermore, when SuDS are designed within the framework of water ethics provided by the UNESCO’s principles (UNESCO, 2011), the scenario could be even brighter for them to be considered for full implementation by the community. This new environmental path helped communities to discover and explore new options to look after the environment beyond a mere comply with the legal requirements from an engineering/technical perspective. This standard approach has alienated human perceptions and its key role in design and planning for a long time. The ethical relationship showed in this research could influence future decision-making of these communities as it is assured by the capacity of prediction of the model (Q2 = 0.437). Thus, it is crucial to understand what barriers community has to raise in order to design and implement educational protocols and procedures, so to deliver a more effective model. Finally, the result showed is strongly high (EBE’s R2 = 0.783), concerning the explanatory capacity of the model, and thus ensures that SuDS would be accepted among those young-aged communities. This result highlights the importance of human aspects in SuDS as an integrated approach to value community perceptions for practice. (2015, 2017) for cities in the Mediterranean region of Southern Europe from a social perspective. Young-aged communities such as the ones targeted in this research presented significant sensitivity towards the implementation of SuDS when setting up environmental, ethical and NBS. This finding supports what it was reported by McKeiver and Gadenne (2005), and Kanchanapibul et al. (2014) about how young people are usually more opened to uptake environmental and ecological practices. In consequence, this research demonstrated at a theoretical and practical levels that communities perceived that the implementation of SuDS could have a wider benefit for the urban environment by linking this benefit to amenity. This work opens a new research line on the impact of human aspects in SUDS implementation, having further implications in design, construction and maintenance. Thus, it would help Southern European cities transition towards more sustainable urban water management, resilient to floods and droughts, following the path of other regions in the World as per referenced by Bos et al. (2012) and Ferguson et al. (2013), amongst other researches. 5. Conclusions The authors would like to thank the neighbouring communities for their participation in this research as well as Cáceres City Council. 5.2. Limitations of this research and future research This study could be also conducted in communities with different average ages in order to identify the barriers for SuDS implementation based upon age ranges. With this aim, we would recommend to extend this methodology to other cities in Southern Europe in order to inform communities across the Mediterranean region and to implement SuDS at a higher scale. In addition, further research could be carried out in other knowledge gaps identified in this paper such as: SuDS perception by engineers, architects and other practitioners in water management related areas in Sotuher Europe. Acknowledgements 5.1. Main conclusions References The combination of the SEM and PLS methods allowed the development of an integral and robust approach to value community perceptions for practice in SuDS in low informed communities on the ecosystem benefits provided by these environmentally focused drainage techniques. Therefore, demonstrating that the wider method proposed by Ramírez and Sañudo-Fontaneda (2018) to deliver more ethical and environmental water management can be translated and tailored to the specific case of SuDS. This new methodology contains transformative potential for change where informed communities of dwellers could implement SuDS through self-organisation, leading the way for resilient water systems in buildings and their surrounding areas in Southern Europe. This finding supports the conclusions from Atkinson et al. (2017) for the specific area of SuDS implementation through community self-organisation. This research reveals that neighbours gave special importance to SuDS when considering the new scenario for water management under climate change conditions in relation with its new water paradigm. 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