Assessment of the food-water-energy nexus suitability of rooftops. A methodological remote sensing approach in an urban Mediterranean area Perla Zambrano-Prado*1, 2, Joan Muñoz-Liesa3, Alejandro Josa3, Joan Rieradevall1,4, Ramon Alamús5, Santiago Gasso-Domingo6, Xavier Gabarrell1,4 1Sostenipra Research Group (Sostenipra 2017 SGR 1683), Institut de Ciència I Tecnologia Ambientals (ICTA-UAB), Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Barcelona, Spain 2 Department of Techniques and Construction, Art, Architecture and Design University Center (CUAAD), University of Guadalajara (UdeG), Calzada Independencia Norte No. 5075, Huentitán El Bajo 44250, Guadalajara, Jal, México 3Department of Civil and Environmental Engineering (DECA), Civil Engineering School (Escola de Camins), Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Jordi Girona 1-3, Building D2, 08034 Barcelona Spain 4Department of Chemical and Biological Environmental Engineering, Universitat Autònoma de Barcelona (UAB), XRB Campus UAB, 08193 Bellaterra, Barcelona, Spain 5 Àrea d'Observació de la Terra – PCOT, Institut Cartogràfic i Geològic de Catalunya, Parc de Montjuïc, 08038 Barcelona, Spain 6Department of Projects and Construction Engineering, Universitat Politècnica de Catalunya BarcelonaTech (UPC), Campus Sud, Building H, Diagonal, 647, 08028 Barcelona e-mail: perla.zambrano@academicos.udg.mx Highlights  A framework to identify roofs for food, water, and energy production is proposed.  Agriculture can be implemented on 7% of roofs to produce tomatoes and lettuce.  A total of 87% of are suitable for integrating rainwater harvesting systems.  A total of 41% of roofs are suitable for integrating photovoltaic panels.  Service buildings and housing are more suitable for agriculture. Abstract This work established a framework to identify and analyze the technical feasibility of roofs for integrating urban agriculture, rainwater harvesting, and photovoltaic systems using various remote sensing. The framework was applied to a Mediterranean region north of Barcelona. Both commercial and nonprofit urban agriculture purposes were considered, and three levels of solar access requirements for different crops were established. The case study included compact and disperse 1 urban forms, residential and nonresidential building uses and various building typologies. It was identified that 7% of the roof area is feasible for tomato and lettuce production, and production could satisfy the average intake of tomatoes for 140,620 persons and the average yearly consumption of lettuce for 14,002 inhabitants. Rainwater harvesting systems could supply 94.26% of the water requirements for lettuce growing in an open-air system; in contrast, 17% of irrigation could be satisfied for tomato production in rooftop greenhouse systems. The results showed a potential for 80% of roof area to be used for rainwater harvesting systems, representing the average yearly water consumption of 29,336 citizens for laundry, showering, toilet flushing, cleaning and irrigation uses. Finally, 41% of the roofs are suitable for photovoltaic panels, representing an average energy consumption of 7,039 citizens. 1. Introduction In recent years, food, water, and energy security have become pressing concerns. To supply these essential needs, cities must depend on their hinterlands (McGranahan & Satterthwaite, 2003), rendering cities unsustainable. Food, water, and energy demand are expected to increase by more than 50% in 2050 (International Renewable Energy Agency, 2015). In this context, cities must improve resource management through sustainable urban planning strategies (Bibri & Krogstie, 2017) considering the food-water-energy (FWE) nexus. 1.1 The nexus between FWE and land in cities Food production requires energy (and water) in many stages of the food system. Water is necessary in irrigation systems, farm machinery, fertilizer production, greenhouse heating and cooling, packing, transportation and distribution (FAO, 2 2015; Midgley et al., 2019). Energy is required to extract, pump, lift, collect, transport and treat water. The agri-food sector consumes 30% of the world’s energy (FAO, 2016). According to projections, by 2050, the demand for energy will double globally (International Renewable Energy Agency, 2015). Energy inputs into the food supply chain are likely to increase in the coming decades, leading to increased energy production necessities (FAO, 2016; International Renewable Energy Agency, 2015). Freshwater is a finite resource, and demand for it has already exceeded local capacities (McGranahan & Satterthwaite, 2003), producing consequences such as water resource overexploitation, stress, and scarcity (Campisano & Modica, 2015; Gosling & Arnell, 2016). In addition, climate change has negative repercussions for water cycles (Arnell & Gosling, 2016; Gosling & Arnell, 2016). Water is also needed during the production processes of energy required for water abstraction, distribution, and treatment, as well as food production and distribution processes. Agriculture consumes 70% of water worldwide (International Renewable Energy Agency, 2015). As the largest consumer of freshwater resources globally, agricultural practices have substantial impacts on water security. Agricultural contamination remains a major source of water pollution. Different agricultural activities, such as fertilization, manure spreading and irrigation, have impacts on surface water and groundwater; for example, approximately 70% of the water pollution in China comes from agriculture in the form of runoff from fertilizers, pesticides and animal waste (International Renewable Energy Agency, 2015). Land is also an important resource concerning the FWE nexus and is a finite resource needed for food production and some forms of energy (e.g., wind and solar 3 farms) and water (e.g., catchment areas, water supply infrastructure and ecosystem services) (FAO, 2015; Midgley et al., 2019). Agriculture already uses 12% of the world’s land surface for crop production (FAO, 2011). Moreover, the use of some of these areas for the agriculture sector would compromise valuable ecosystem services and biodiversity (FAO, 2011). The production of local resources in cities is a key strategy to ensure the resilience of and accessibility to food, water, and energy. In the case of food systems, urban agriculture (UA) is an approach to satisfying food demand in cities, with advantages in different fields, such as social (e.g., social inclusion), environmental (e.g., transport emissions reductions) and economic impacts (Lupia et al., 2017; Mok et al., 2014; Parece et al., 2016; Thomaier et al., 2015). In addition, rainwater harvesting systems (RWHSs) are strategies for managing water resources, providing sustainable water cycles and minimizing water tap demand (Jha et al., 2014; Sojka et al., 2016). Regarding the energy sector, bioenergy, solar photovoltaic (PV) or solar thermal panels can satisfy a relevant part of energy demands (Corcelli et al., 2019; Midgley et al., 2019). In urban areas, land is an increasingly scarce resource, and the high price and competition for residential buildings, industrial growth or infrastructure construction represent some of the main threats to the local production of food, water, and energy (Benis et al., 2018; Midgley et al., 2019). In this sense, since rooftops cover half of the impermeable surfaces in cities (Farreny et al., 2011), there are many areas that could be potential spaces for producing food (e.g., through the installation of open- 4 air agriculture or rooftop greenhouses (RTGs)), water collection (e.g., rainwater harvesting systems) and energy (e.g., solar photovoltaic or solar thermal panels). 1.2 Rooftops as productive spaces Rooftops have the potential to improve urban metabolism by producing resources (for instance, food, water, and energy) (Corcelli et al., 2019). In the case of promoting local food production, the environmental benefits are mainly related to a reduction in transportation requirements and the consequent environmental impacts (Cerón- Palma et al., 2012; Sanyé-Mengual et al., 2013). The transformation of rooftops into productive spaces is becoming a common practice in many cities worldwide (Gundula Proksch, 2016). New York, Paris, Vancouver, Toronto, Barcelona and Melbourne are some of the cities that have started to convert roofs into potential productive spaces, integrating food production and solar photovoltaic and solar hot water systems (City of Toronto, 2009; The New York City Council, 2010; Ville de Paris, 2019). Different criteria must be considered to assess the feasibility of integrating FWE systems. Climatic conditions are crucial to determining the potential integration of FWE systems. For food production, sunlight access is the main climate parameter for determining the suitability of rooftop urban agriculture (RUA), based on the requirements of the selected horticultural crop (FAO, 2013). Regarding the integration of RWHS, it is necessary to consider the rainwater potential, which refers to the entire amount of rainfall in the area under consideration (Worm & van Hattum, 2006). Solar energy, as in the case of RUA, requires solar radiation on rooftops, and 5 shadows from the built environment play an important role in identifying potential areas. Roof characteristics, such as their geometry, area, slope, solar radiation and materials, also need to be considered. Additionally, climatic conditions are determinant factors for assessing the suitability for integrating food, water, or energy production systems in cities (Carter & Keeler, 2008; Orsini et al., 2016; Salvador et al., 2019; Toboso-Chavero et al., 2018; Villarreal & Dixon, 2005). In the following section, the literature concerning roof characterization using remote sensing (RS) to assess the potential integration of FWE systems is described. 1.3 Remote sensing data to assess rooftops for FWE production Remote sensing has been utilized to assess the feasibility of roofs to implement FWE systems. Roof data acquired using light detection and ranging (LiDAR) sensors have been applied to assess the UA, RWHS, or PV potential of roofs. LiDAR systems allow us to obtain geometric data and to compute solar access to roofs. Area, slope, and solar radiation have been the principal criteria for assessing UA, RWHS or PV systems (e.g., Berger, 2013; Haberman et al., 2014; Kodysh, Omitaomu, Bhaduri, & Neish, 2013b; Lupia et al., 2017). In addition to roof geometry, some works have integrated other criteria, such as maximum building floors (Berger, 2013) and building height (Saha & Eckelman, 2017), to identify hospitable heights for plants and logistical safety concerns with rooftop access for people for UA purposes; roof materials (Nadal et al., 2017) and construction year (Berger, 2013) have also been identified. Roof materials are important criteria since roofs must be resistant to matching the load-bearing requirements of RTGs or open-air systems. Roof 6 materials are also relevant criteria for integrating RWHS, considering that the quality and quantity of rainwater harvested are significantly affected by roofing materials (Farreny et al., 2011). Table 1 illustrates a summary of works assessing UA, RWH, and PV potential on roofs using remote sensing data. 7 Table 1. Characterization of rooftops using remote sensing and geographic information systems (GIS) to integrate UA, RWH, or PV. Scale of Roof data acquired with remote Resource Data collection Building use study sensors production and GIS Software Reference case technique used C I R E I Re W A S SR M BF BH Building Berger (2013) F N footprint solar ArcGIS       map from LiDAR Land use map Haberman et al. F Ci ArcMap   Google Earth (2014) Saha & DSM from F Ci ArcMap       Eckelman LiDAR (2017) DSM from Nadal et al. LiDAR; spectral (2017) F Co QGIS      signatures from LWIR data Aerial imagery Grant et al. W Co Esri     LiDAR data (2017) Satellite image Lupia et al. W Ci DEM from QGIS    (2017) LiDAR Building Radzali, Shafri, W N footprint from ArcGIS      Norman, & satellite imagery Saufi (2018) Bayrakci Boz, DSM from Calvert, and R. E Ci ArcGIS        LiDAR S. Brownson (2015) Brito, Gomes, DSM from Santos, & E Ci ArcGIS      LiDAR Tenedório (2012) 8 Google Earth Khan & Arsalan E N ENVI EX      satellite imagery (2016) Kodysh, Building Not Omitaomu, E Co footprint from     specify Bhaduri, & LiDAR Neish (2013) Wiginton, E Ci Aerial imagery ArcGIS    Nguyen, & Pearce (2010) Resource production: food (F), water (W), and energy (E). Scale of the studies: city (Ci), county (Co), and neighborhood (N). Building use: commercial (C), industrial (I), residential (R), educational (E), institutional (I), recreational (Re), and warehouse (W). Roof data acquired with remote sensors: area (A), slope (S), solar radiation (SR), material (M), building floors (BF), and building height (BH). 9 In Table 1, we can observe that there is a gap in the assessment of roofs to integrate FWE production systems with a holistic approach since all of the pervious works described focused on a single vector (food, water or energy). In addition to data collection, most works have used LiDAR sensors, digital surface models, and building footprints. Regarding the information analysis, previous works have mostly used ArcGIS, QGIS and ArcMap software. The studies analyzed in Table 1 considered case studies at three different scales: city, county, and neighborhood. With respect to building use, most of the studies considered residential and commercial buildings to integrate UA, RWHS or PV systems. However, it is important to consider the integration of these systems with other types of buildings, such as educational buildings, in which UA is also a strategy to promote environmental education and social cohesion, considering that many schools currently have meal services (Nadal et al., 2018), and according to the main goal of the Global Education 2030 (Agenda UNESCO), to acquire sustainability competencies as a core of Education for Sustainable Development (Leicht et al., 2018). As we can observe in Table 1, the roof material is the criterion with the lowest presence to assess the potential of UA, RWHS or PV systems. Additionally, sunlight access has received little consideration in the literature, referring to the assessment of UA on rooftops. However, this criterion is critical for growing food. Between 20 and 30% of solar radiation is lost in conventional greenhouses (Castilla, 2005) due to material transmissivity, which could decrease by up to 50% in integrated rooftop greenhouses due to the additional building shadows (Montero et al., 2017). Thus, 10 sunlight availability for crops in the urban agriculture context should be specifically addressed since it could be a relevant limiting factor for plant growth. Similarly, there is a gap in the published literature with respect to an integrated framework for assessing the viability and potential of roofs for UA, RWHS, and PV system implementation using remote sensing technology. In this context, the main objective of this study is to assess the feasibility of roof areas to integrate food production, rainwater collection, and solar energy systems in cities using remote sensing technologies. In this regard, the specific objectives are 1) to establish a framework for evaluating the potential integration and self- sufficiency of FWE systems for commercial and nonprofit purposes; and 2) to apply the procedure in a Mediterranean area. 2. Materials and methods 2.1 Case study The case study is located in a Mediterranean region in the El Vallès Occidental, north of the Spanish city of Barcelona. The Vallés Occidental is composed of 23 municipalities, and some of them are part of the Metropolitan Area of Barcelona (MAB). This area was chosen due to its sunny weather predominance, which offers a strong potential to integrate both RTGs, open-air rooftop agriculture and solar energy systems. This climatic area can also offer winter crop production without requiring any active system to heat greenhouses. There are recent initiatives from the city council to promote productive roofs and institutional and citizen awareness of UA. Additionally, the case study area includes a pilot RTG at the Institute of 11 Environmental Science and Technology (ICTA) and the Catalan Institute of Paleontology (ICP) building, where different types of crops have been produced since 2015 by the Fertilecity project team (Fertilecity, 2018). Regarding rainwater, several municipalities started to approve water-saving regulations (Domènech & Saurí, 2011), including RWHS, since the amount of precipitation makes the integration of these systems in this area suitable. 2.2 Criteria to integrate FWE systems for commercial and nonprofit purposes The work developed by Nadal et al. (2017) and Toboso-Chavero et al. (2018) was used as a basis for developing the methods of this work and adding other aspects obtained from the literature (see Appendices). The previous authors considered in their methods the short-term potential implementation of the systems on industrial and residential rooftops. The work developed by Toboso-Chavero et al. (2018) assessed the potential integration of the FWE nexus. However, roof data acquisition was conducted by means of architectural layouts, implying difficulties for the application at larger scales, such as counties or cities. The methodology proposed by Nadal et al., (2017) uses GIS and remote sensing to obtain and manage information to identify suitable roof areas. However, it only focuses on RTGs on industrial roofs for commercial purposes. In this regard, both methodologies present a lack of some criteria for assessing the potential of integrating FWE systems for commercial and nonprofit purposes. For this reason, a literature review was performed to detect and establish the needed criteria to broaden the focus of the framework for this work (see Appendices). Figure 1 summarizes the phases of the methodologies used for this work: phase 1 study area characterization was based 12 on the work developed by Toboso-Chavero et al. (2018) and enhanced with a literature review, phase 2 urban and architectural requirements, and phase 3 self- sufficiency indicators. The criteria of phases 2 and 3 were based on Nadal et al. (2017) and extended with a literature review. Figure 1. Phases to evaluate the feasibility of roof areas to integrate food production, rainwater collection, and solar energy systems in cities based on Toboso-Chavero et al. (2018) and Nadal et al. (2017). 2.3 Application to a case study The three phases and criteria defined in the previous section were applied to evaluate the feasibility of roof areas to integrate food production, rainwater collection, and solar energy systems in the case study. QGIS software was used to manage and analyze the information. The application of each phase to the case study is described in the following subsections. 2.3.1 Phase 1: Study area characterization a) Urban features. GIS was used to define the boundaries and to conduct a spatial analysis regarding roof area, land use, and the typology of buildings. 13  To identify land use and building typologies, two vector layers were used: (1) The Urban Planning Map of Catalonia (UPMC), downloaded free from the Department of Territory and Sustainability (2019), and (2) roof polygons obtained from a previous work developed by the authors. Data from these two layers were integrated using the tool join attributes by localization. b) Climatic conditions. Datasets from public websites were used to obtain climatic conditions.  The rainfall dataset was obtained from a weather station (Rural Cat, 2019) over a period of 21 years (from 1996 to 2017).  The average annual solar radiation was obtained from an atlas of solar radiation in Spain (Sancho et al., 2012). c) Resource consumption. Food, water, and energy demand was acquired from regional and local public reports (Ajuntament de Barcelona Medi Ambient i Serveis Urbans, 2016; Departamento d’Agricultura ramaderia pesca i alimentació, 2017; Medi Ambient i Serveis Urbans-Ecologia Urbana Agència d’Energia de Barcelona, 2019). 2.3.2 Phase 2: Urban and architectural requirements a) Urban. To address legal requirements, Barcelona Metropolitan General Plan regulations and building codes (Metropolitan area of Barcelona, 2018) were consulted. Building uses were identified from the layers explained previously in section a) of Phase 1: Study area characterization. 14 b) Architectural. Remote sensing technology was used to obtain geometric characteristics, solar radiation values, and material classes of roof materials.  Geometric and solar radiation characterization. LiDAR remote sensing technology was used to characterize the geometric properties of roofs. Then, solar irradiation on surfaces was computed with GRASS software, version 7.4. This process considered all of the shadow effects in the surrounding area. The airborne sensor Leica ALS50-II (LiDAR) was used on a flight conducted in 2013. Based on high density (4 points/m 2), a digital surface model (DSM) with a spatial resolution of 0.5 m pixels was obtained. This model contained all of the geometric characteristics of the terrain and buildings, such as area and slope. The model also included objects on rooftops (e.g., air conditioning installations) and objects that can produce shadows over roofs. The point cloud was classified as ground, vegetation, buildings, and other objects that were not rooftops. Then, points that were considered noisy, such as powerline wires, points with low intensities, and points in the air, were excluded. The average annual solar irradiation was computed with the r.sun library in GRASS software, version 7.4, by adding direct and global radiation received at each pixel of the DSM every hour for one day of each week over the whole year. In this calculation, all of the shadows generated by buildings, vegetation, topographies, etc., were considered.  Roof material characterization. Previous work developed by the authors on rooftop classification was used, the work used hyperspectral data acquired during a day flight in 2018 with two imager spectrometers: an Airborne 15 Imaging System for Different Applications (AISA) Eagle II and a Thermal Airborne Spectrographic Imager 600 (TASI-600). Data obtained by remote sensors were processed using a cluster algorithm (K-means) to classify roof material data. In the present work, two classes of roofs were totally excluded to integrate FWE systems. Fiber cement roofs were excluded because of their low load-bearing capacity to support RTGs, open-air UA, and PV panels, as well as health risks for collecting rainwater; additionally, green roofs were excluded considering that they are already productive spaces. It should be noted that the location and weight of the water tank for RWHS were not considered in this work. Data layers with geometric characteristics (area and slope) and nongeometric characteristics (solar radiation, roof materials, use and typology of buildings) were integrated into a single GIS layer with QGIS software using the joint attributes by location tool. Once all of the data were integrated into a single layer, potential roofs were identified using select by expression. With this tool, roof requirements for each system (food, water, and energy) were filtered and then extracted into a new layer of roof polygons that fully fit the established criteria. In this work, potential areas for food production were prioritized with respect to energy production. 2.3.3 Phase 3: Self-sufficiency indicators a) Resource production. Tomatoes and lettuce were considered suitable crops due to the amount of household consumption data in the region of the case study. Tomatoes were chosen because they are the second most consumed vegetable in Barcelona, after potatoes (Departament d’Agricultura Ramaderia, Pesca i 16 Alimentació, 2017), which cannot be produced in hydroponic RTG systems. Lettuce represents the most consumed leafy vegetable (Departament d’Agricultura Ramaderia, Pesca i Alimentació, 2017). Soil-less systems for both open-air and RTG conditions were considered. Food-crop yield was obtained for RTGs and open-air studies from Barcelona. (average yield values are listed in the Appendices Table A1). To calculate the rainwater harvesting potential, runoff coefficients of 0.80 (ceramic), 0.90 (metals), and 0.60 (gravel) were considered (Farreny et al., 2011) according to roof materials in the study case. Multicrystalline silicon (multi-Si) technology was chosen for PV because it is the most common in the market (Paiano, 2015) for estimating energy production, and a PV panel efficiency of 14.9% was considered (Toboso-Chavero et al., 2018). To determine the potential production of food, water, and energy, the total feasible rooftop area (m2) obtained from the application of the framework using remote sensing technology (Phase 2) was used in the following equations:  To quantify food production, Equation 1 was used, where 𝐹𝑃 is the yearly food production (kg), ∑ 𝑎 is the total feasible rooftop area (m2), and 𝑌𝑉 is the average yield value (kg/m2). 𝐹𝑃 = ∑ 𝑎 × YV To quantify food production, Equation 1)  To determine the water harvesting potential, Equation 2 was used, where 𝑊𝐻𝑃 is the yearly amount of rainwater harvested from rooftops (L), ∑ 𝑎 is the total feasible rooftop area (m2), 𝑃𝑡𝑜𝑡 is the total annual precipitation (mm), and 17 𝑅𝐶 is the runoff coefficient (harvesting efficiency of the system, depending on roof material; see above). 𝑊𝐻𝑃 = ∑ 𝑎 × 𝑃𝑡𝑜𝑡 × 𝑅𝐶 (2)  To determine solar energy production, Equation 3 was used, where 𝑃𝑉𝐸𝑃 is the yearly amount of photovoltaic energy production (kWh/m2/year), ∑ 𝑎 is the total feasible rooftop area (m2), and 𝑆𝑅 is the global solar radiation potential (kWh/year). 𝑃𝑉𝐸𝑃 = ∑ 𝑎 × 𝑆𝑅 (3) b) Self-sufficiency. Fresh tomato and lettuce consumption of 14.9 and 4.4 kg/per capita/year, respectively, was considered (Departamento d’Agricultura ramaderia pesca i alimentació, 2017). Water consumption of tomatoes and lettuce production in Barcelona in open-air and RTG systems and the soil-less method were considered for calculations of water self-sufficiency for irrigation crops (see Appendices Table A2). In addition, the water demand for domestic use was acquired from the El consum d'aigua a Barcelona L'aprofitament i els usos dels recursos hídrics (water consumption in Barcelona The use of water resources) report, considering an average of 105 L/household/day, including laundry, showering, toilet flushing, cleaning and irrigation (Ajuntament de Barcelona Medi Ambient i Serveis Urbans, 2016). The average yearly energy consumption over a period of 19 years was estimated from data obtained from the Balanç d’energia amb efecte d’hivernacle i emissions de gasos de Barcelona 2017 (Energy Balance and Greenhouse Gas 18 Emissions of Barcelona 2017) report (Medi Ambient i Serveis Urbans-Ecologia Urbana Agència d’Energia de Barcelona, 2019). To determine the potential of self-sufficiency (# persons), the following equations were used.  To quantify potential food self-sufficiency, Equation (4) was used, where 𝑃𝐹𝑆𝑆 is the self-sufficiency of total food production, FP is the food production (obtained from Equation 1), and FD is the average food demand (kg/per capita/year). 𝑃𝐹𝑆𝑆 = FP/FD (4)  To quantify potential water harvesting self-sufficiency, Equation (5) was used, where 𝑃𝑊𝑆𝑆 is the self-sufficiency of the total water collection of roofs, WHP is the water harvesting potential (obtained from Equation 2), and WD is the average water demand (L/per capita/year). 𝑃𝑊𝑆𝑆 = WHP/WD (5)  To quantify potential solar energy self-sufficiency, Equation (6) was used, where 𝑃𝐸𝑆𝑆 is the self-sufficiency of total PV energy production, PVEP is the total photovoltaic energy production (obtained from Equation 3), and ED is the average energy demand (MWh/per capita/year). 𝑃𝐸𝑆𝑆 = PVEP/ED (6) 19 3. Results 3.1 Framework for assessing the feasibility of roofs to integrate FWE systems for commercial and nonprofit purposes Figure 2 illustrates the framework developed to identify feasible roof areas to integrate food, water, and energy systems in cities. It is divided into three phases: study area characterization (phase 1), urban and architectural requirements (phase 2), and self-sufficiency indicators (phase 3). Figure 2 also shows the methods for data acquisition for each criterion, mainly based on RS. Each of the phases is described below. Phase 1 Study area characterization. In this phase, the characteristics of the study area are obtained, and phase 1 is divided into the following three steps. 1) Urban features. Data about population density, urban form, building use, and building typology are needed to conduct a spatial analysis. To acquire population density data, a public dataset can be used. Data on urban form are acquired using a literature review, and GIS information concerning the use and typology of buildings is acquired using GIS. 2) Climatic conditions. Since climatic conditions play an important role regarding the potential of FWE systems, a minimum solar radiation of 1,000 MJ/m2/year is needed for food production (microgreens) in open-air systems and a minimum of 300 mm/year of rainfall. To obtain this information, datasets from public websites are used. 20 3) Resource consumption. Food, water, and energy consumption are needed to determine the resource quantity required and used in phase 3. Both datasets and a literature review are used to obtain resource consumption information. Phase 2 Urban and architectural requirements. This phase is divided into two steps and various criteria by each step. For the architectural step, Figure 2 shows the minimum requirements needed to integrate FWE systems. 1) Urban requirements. Building laws and land uses must be considered to ensure that the new infrastructures meet legal requirements. These data are obtained using a literature review of urban and architectural codes of the city. It is important to know the building use to establish the purpose of the infrastructure. Two purposes are proposed: commercial and nonprofit (for self-consumption and including educational and social objectives). Retail and industrial buildings are more suitable options for commercial buildings, while residential buildings, schools, and public services buildings are desirable for nonprofit goals. Data concerning building use, as in phase 1, are obtained using GIS. 2) Architectural requirements. This step considers four criteria: area, slope, solar access, and roof material. The first three (geometric characteristics) are acquired using a LiDAR sensor, and the last one is acquired using hyperspectral remote sensing. Each of the criteria is described below. Roof area. A minimum roof area is needed for food and energy purposes. For commercial energy infrastructures, a minimum of 100 m2 is needed, and in the case of food production, a minimum of 500 m2 is needed. With respect to nonprofit goals, 21 energy systems require a minimum of 24 m2, and food systems require a minimum of 13 m2. Roof slope. This criterion is divided into two: if the roof is flat with a maximum slope of 10%, food production systems can be integrated; if the roof slope is greater than 10%, energy and rainwater harvesting systems can be implemented. Solar access. This criterion is divided into four groups, one for energy systems, in which a minimum of 1,900 MJ//m2/year is needed, and three for food production, according to the crop and food production system. The three levels are high, medium, and low for tomatoes; leafy vegetables, strawberries, and seedlings; and microgreen crops, respectively. Each level of solar access is divided into two corresponding food production systems: RTGs or open-air. Therefore, the minimum requirements of solar access for food systems are as follows: tomatoes growing in RTGs at 4,700 MJ//m2/year and those growing in open-air at 2,200 MJ//m2/year; leafy vegetables, strawberries, and seedlings cultivated using RTGs at 3,500 MJ//m2/year and those using open-air at 2,000 MJ//m2/year; and microgreens produced in RTGs at 1,700 MJ//m2/year and those produced in open-air at 1,000 MJ//m2/year (see Appendices Table A3). Roof material. The load-bearing capacity to support the weight of the system is essential, and a minimum load-bearing capacity of 200 kg/m2 is required for crops. A lower capacity can be suitable for energy systems. The load capacity of the roof does not condition rainwater collection in this sense, and any material has potential for water systems with special attention paid to health risks according to roof materials and the future use of water resources. It is necessary to consider the part 22 of the building in which the storage tank will be located. If the tank is placed on the roof, load-bearing must be considered. This framework does not consider water location and weight. 3) Self-sufficiency indicators. This phase is divided into two steps: resource production and self-sufficiency potential. Food production consists of a variety of systems and methods for growing crops that affect growing values and water requirements for irrigation; in the case of RWH, the quantity of rainwater harvested from roofs is affected by roofing materials; and for photovoltaic systems, solar radiation influences the amount of energy produced. Thus, the first step uses different equations, in which the total roof area that fulfills the criteria for phase 2 identified as potential and other parameters are used to obtain the production per year of food, water or energy. In the second step, self-sufficiency potential uses results derived from the first step and consumption of resource data to obtain how many people can be supported by the food, water, or energy supplied. 23 24 Figure 2. Workflow proposed to identify feasible roof areas to integrate urban agriculture, rainwater harvesting, and photovoltaic energy systems. The sunlight access criterion of the phase 1 study area characterization refers to the minimum energy radiation for producing microgreens. 3.2. Assessing the rooftop potential to integrate FWE systems in a Mediterranean area 3.2.1 Study area characterization Figure 3 illustrates the study area location in the Valles Occidental region north of Barcelona, Spain, and its main characteristics. The study area is approximately 15 km2 with compact and diffuse urban forms and a total of 3 km 2 of roofs ranging from 0.25 m2 to 13,480.5 m2. Roofs were in different land uses: housing (75.0%), industrial (16.6%), services (5.9%), mix (0.6%), and non-urban (0.5%). Some roofs were not identified with respect to the land use location (1.5%). Different building typologies of housing were found: 4.3% corresponded to low rise buildings in the founding nuclei and centers populations (R1); 25.1% to compact and open building blocks, corresponding to historical growth of a structure before 1950 (R2); 0.4% to multifamily blocks with interior patios, corresponding to modern extension (R3); 12.9% to blocks or towers configured from an isolated multifamily building (R4); 38.4% to detached houses for single families (R5); 18.6% to terraced houses for single families (R6); and 0.1% to building blocks for public equipment housing (SD). The average global radiation was 4.6 kWh/m2/day, and the average annual rainfall over the study area was 592 mm. 25 Figure 3. Location and study area characterization. Building typologies of housing in the study area included low rise buildings in nuclei and center populations (R1), compact and open building blocks (R2), blocks with interior patios (R3), isolated blocks or towers (R4), detached houses (R5), terraced houses (R6), and building blocks for public equipment housing (SD). Food production is allowed for self-consumption. Land use types do not include agriculture use. For this reason, UA for commercial purposes is not permitted. On some rooftops, RTGs were not permitted due to the maximum height and volume restrictions. In this sense, a case-by-case review by local technicians is required. To integrate RWHS and PV systems on rooftops, no restrictions were found. According to the Spanish Technical Building Code, it is mandatory to include minimums for electricity and sanitary hot water self-sufficiency in new buildings and developed 26 building extensions (except for residential buildings) when the constructed area exceeds or is increased by more than 3,000 m2. 3.2.2 Analysis and quantification of potential roofs for integrating FWE systems After applying the proposed framework, potential roofs were obtained. In this section, the results are presented in layers according to each system studied (UA, RWHS, and PV). A total of 823 roofs and 0.2 km2 were identified for integrating UA (Figure 4a), representing 7% of the total roof area. The results of potential areas were identified by different colors according to the growing system and crop type, as shown in detail in Figure 4b. The distribution of potential roofs was as follows: RTGs for tomato production 100,610.5 m2 (49.0%); RTGs for lettuce crops 7,410.2 m2 (3.6%); open-air for growing tomatoes 97,043.25 m2 (47.3%); and open-air for lettuce production 89 m2 (0.04%) (see Figure 4c). 27 Figure 4 (a) Potential roofs to integrate rooftop agriculture over the total study area and distribution of roofs related to growing system and crop type. (b) Details of identified potential roofs on educational buildings from the Autonomous University of Barcelona. (c) Distribution of potential roofs according to growing system and crop type. Figure 5a shows potential roofs for UA and their location distribution by land use. It can be observed that most rooftops were located in service land use areas. Regarding each growing system distribution of feasible rooftops according to land use, they were as follows: RTGs for harvesting tomatoes: 56.6% services, 41.5% industrial, 0.7% housing, and 1.2% mix; RTGs for growing lettuce: services 91.1% and 8.9% housing; open-air system for tomatoes: 59.4% housing, 23.4% services, 14.3% industrial, 0.4% mix, and 2.5% without data; and finally, open-air lettuce: 28 services 68.9%, and 31.1% without data. Figure 5b also shows the potential roof distribution by building typology of housing use (60%). Most roofs belonged to typology R4 with 61.1%, followed by R6 with 30.1%, R2 with 6.1%, R5 with 2%, and R3 with the fewest potential roofs with 0.5%; no roofs were identified as typology R1. Figure 5. (a) Potential roofs identified to integrate rooftop agriculture and their distribution by land use and (b) by building typology of housing. Figure 6 shows the results of tomato and lettuce production and self-sufficiency. The total roof area for tomato crops could produce 2,096.65 tons per year, 73.4% by RTGs and 26.6% by open-air systems. Production could satisfy the average intake for 140,620 persons, representing 108% of the population of four municipalities over the study area (Figure 6a). Regarding lettuce crops, the results showed that a total of 62.3 tons per year could be produced, 99.5% by integrating RTGs and 0.5% by open-air systems. This production could satisfy the consumption of 14,000 inhabitants, representing 104% of citizens of one municipality in the study area (Figure 6b). Self-sufficiency analysis for crop irrigation showed that the integration of RWHS could supply 94.3% of the total water requirements for lettuce crops in open-air 29 systems and 82.1% for the same crop in RTGs, as well as 63.2% for tomatoes in open-air and 17.0% for the same crop in RTGs (Figure 6c). Figure 6. Rooftop urban agriculture production and water self-sufficiency by crop. (a) Tomato production in open-air and RTG systems and self-sufficiency. (b) Lettuce production in open-air and RTG systems and self-sufficiency. (c) Water self-sufficiency for irrigation regarding the type of crop and system. Potential areas for integrating UA were considered for rainwater collection for crop irrigation. 30 Regarding rainwater harvesting potential, a total of 37,762.00 roofs and 2.4 km2 were identified for integrating RWHS (Figure 7a), representing 80% of the total roof area. These results did not consider potential roofs (7%) for agricultural irrigation (shown in Figure 6). The results of potential roofs for integrating RWHS are shown in blue. Details of the identified roofs of housing and service buildings are shown in Figure 7b, and potential roofs for different land uses are shown in Figure 7c. Most rooftops belonged to industrial (43.1%) and housing (40.2%) uses; service rooftops represent 14.1%; some polygons (1.6%) did not show information related to land use; and FWE roofs were mixed and nonurban at 0.7% and 0.4%, respectively. The results showed that a total of 1,124,000 m 3 of rainwater per year could be collected on roofs, representing the average consumption of approximately 29,340 citizens for laundry, showering, toilet flushing, cleaning and irrigation uses (Figure 7d), representing 89% of the population of one of the municipalities in the study area. 31 Figure 7. (a) Potential rooftops for integrating RWHS over the study area. (b) Details of identified potential roofs in housing and services buildings. (c) Potential roofs identified to integrate RWHS and their distribution by land use. (d) Water self-sufficiency for laundry, showering, toilet flushing, cleaning and irrigation (no agricultural) uses. A total of 8,088 roofs and an area of 1.3 km 2 were identified to integrate PV panels (Figure 8a), representing 43% of the total roof area. Potential roofs were assigned the yellow color. Details of the identified roofs of housing and service buildings are shown in Figure 8b. Roofs for PV panels were in different land uses, as shown in Figure 8c. Most of the rooftops belonged to industrial (65.1%); housing roofs represented 20.1%; service rooftops represented 12.6%; polygons without land use information represented 1.3%; and FWE roofs were mixed and nonurban at 0.6% and 0.4%, respectively. 32 The results showed that a total of 80,400 MWh/m2/year could be produced on roofs, representing the average consumption of 7,039 citizens (Figure 8d) or 52% of the population of one municipality in the study area. Figure 8. (a) Potential rooftops for solar energy production by PV panels over the study area. (b) Details of identified potential roofs in housing and service buildings. (c) Potential roofs identified to integrate PV panels and their distribution by land use. (d) Energy self-sufficiency in the study area. 4. Discussion 4.1 Framework for assessing the feasibility of roofs to integrate FWE systems for commercial and nonprofit purposes 33 The proposed framework represents a novelty due to the integration of food production, rainwater harvesting, and solar energy criteria to assess the feasibility of rooftops to implement these systems using airborne sensors, in addition integrates criteria which can allow to detect potential roofs for different purposes, and the definition of climatic conditions including different levels of sunlight access on roofs also constitute they are also an improvement over other study. The main contributions of the work carried out are described below.  The integration of food, water, and energy systems. There is no framework that integrates the assessment of these three systems from the perspective of using remote sensing technology for data acquisition. According to the review of the existing works related to the subject, all them considered one single vector: food production (Berger, 2013; Haberman et al., 2014; Nadal et al., 2017; Saha & Eckelman, 2015), water (Grant et al., 2017; Lupia et al., 2017) or solar energy (Bayrakci Boz et al., 2015; Brito et al., 2012; Khan & Arsalan, 2016; Kodysh et al., 2013; Wiginton et al., 2010).  Use of remote sensing. This technology allowed us to characterize the study area and to acquire high spatial resolution and reliable data over large areas, including city scales. RS allowed to apply systematic and automatic procedures which can reduce the capture time. Regarding the use of remote sensors to identify roof materials the present study used two remote sensors, the TASI-600 (LWIR) and the AISA Eagle II (VIS-NIR) sensors, for data acquisition. The use of these sensors together could allow for more reliable data to discriminate among materials. Nevertheless, the cost of acquiring 34 data with this technology (if no public database is available) can be expensive and could restrict the application of the framework is available could restrict this part of the application of the framework. Alternatives such as acquire high spatial resolution image satellite can be less expensive, however, data readability could be lees due to building sizes, in this regard satellite resolution must be specially considered.  Solar access for crops. The definition of light requirements regarding different crops allowed us to include, rather than discard, roofs that could also be productive even with low average solar radiation. Most works that considered solar access excluded roofs with low solar radiation (Berger, 2013; Nadal et al., 2017, 2019; Sanyé-mengual et al., 2015; Toboso-Chavero et al., 2018). These practices omitted potential areas for growing leafy crops, which require less light than tomatoes, which was the main crop used to identify potential areas in previous works. Some studies did not consider the light access criterion (e.g., Saha & Eckelman, 2017), which is critical to assess the viability of roofs for growing food. If this criterion is not considered, potential areas can derive less realistic results when no artificial light resources are considered, since the built environment can generate shadows on rooftops, and solar radiation is needed for crops.  Integration of commercial and nonprofit goals. The integration of these two uses is a novelty and allows us to include not only large areas for economic feasibility in industrial, commercial, or retail buildings, as most of the works performed (Berger, 2013; Nadal et al., 2017; Saha & Eckelman, 2017; Salvador et al., 2019; Sanyé-mengual et al., 2015; Sanyé-Mengual et al., 35 2016), but also smaller polygons for educational, social integration, and self- consumption purposes (Nadal et al., 2019), especially when roofs of a municipality or city scale are evaluated. The minimum roof areas used in this work might be geographically sensitive, and in some cases, it might be necessary to perform additional analysis, especially, an economic analysis for commercial purposes. Even so, this framework can be used as a basis to assess roofs viability.  Building use criteria. The consideration of this criterion allowed us to characterize study areas by identifying building use. In addition, a quantitative analysis of the distribution of potential roofs according to building use was performed in this work; however, a deeper analysis regarding the relationship of the system purpose (commercial and nonprofit) with building use would be interesting. It was found low percentage (1.5%) of roofs without data information about land use, which could be due to the use of airborne datasets provided by different institutions and acquiring data on different dates which could generate some outdated data. 4.2 Rooftop potential to integrate FWE systems in a Mediterranean area Services and housing rooftops were the most feasible for integrating RUA, which can be attributed to the structural requirements. Industrial roofs were present in a larger proportion of the total study area than service roofs. However, a smaller proportion of potential industrial roofs was observed to integrate UA. Generally, this type of roof is characterized by large polygons and a high amount of solar radiation, which makes the integration of RTGs feasible. Even so, most of them have low load- 36 bearing capacity, such as metal decks and light or metal coverings and fiber cement sheets. Low-resistance materials are a barrier to integrating UA systems on roofs in the immediate term, and an adaptation concerning structural reinforcement of the roof is needed for it to be a candidate for UA integration. Similar results were found by Nadal et al. (2017) in an industrial zone of the Metropolitan Area of Barcelona, where roofs met the minimum area and solar radiation but not the load-bearing capacity of the roof material. Service and housing buildings are usually composed of higher load-bearing capacity structures, with nontransitable roof systems composed of concrete and gravel surface finishing or other concrete systems finished with asphalt sheets. These materials meet structural resistance requirements but require a roof finishing adaptation to allow the installation and operation for growing and harvesting food. Regarding housing buildings, roofs were characterized by a variety of the previous roof systems described, in addition to ceramic roofs, which can fulfill the resistance criterion because concrete systems in general are presented in ceramic roof finishing covers. However, some roofs with too much slope could have metal structures that might not fulfill the resistance requirement. It was noted that the R4 building typology showed greater potential than R5, R2, and R6, which had higher representation over the total study area. These typologies showed a large number of shadows and pronounced slopes, especially the R6 housing typology. Blocks or towers configured from an isolated multifamily building were found to be the most promising typology for RUA purposes. The work developed by Toboso-Chavero et al. (2018) already concluded that block-isolated multifamily buildings have the potential to integrate 37 open-air and RTG systems. These results show that building morphology and urban configuration could play an important role in rooftop UA potential. In relation to food production, this work found that 7% of roofs in the study area could integrate urban agriculture. This result is similar to those obtained by Sanyé- Mengual, Cerón-Palma, Oliver-Solà, & Montero, Juan Ignacio Rieradevall (2015) in a logistic and industrial park area located in Barcelona where a 8% of roofs were found feasible for RTGs representing 13 h. Compared to Sanyé-Mengual, Cerón- Palma, Oliver-Solà, & Montero, Juan Ignacio Rieradevall (2015) work the feasible roof area in the present study was 7 ha greater, even so, tomato production was not significantly higher in our results, this difference could have occurred because 26.6% of roofs were feasible for open-air systems, and the yield production was less than that with RTG systems. With respect to the RWHS for crop production, the results showed that it could be possible to supply at least 63.2% of the water requirements for lettuce crops using both RTGs and open-air and tomatoes growing in open-air systems. The results of this work are better, especially for lettuce growing in open-air systems, which found 94.3% water self-sufficiency compared with that found for a case study in Rome for horticulture gardens, where 57% of water could be supplied by rainwater collection on rooftops (Lupia et al., 2017). These differences can be related to the following reasons: (1) catchment area, this work considered all roof areas as catchment surfaces; (2) RC value, this work considered a variety of RCs (0.9,0.8, and 0.6) according to roof materials in the area, while a conservative value of 0.6 was considered for all potential areas by Lupia et al. (2017); and (3) water requirements, 38 establishment of water consumption for irrigation plays an important role, and soil- less systems, such as perlite, require less water than soil based systems. The water supply by rainwater for growing tomatoes using RTG systems was much lower than that in previous experimental works performed in Barcelona, the water efficiency for crops can be improved with the use of leached recirculation, including an extension of the crop production season to obtain 80% of the water requirements for tomato crops (Sanjuan-Delmás et al., 2018) which were no considered in this work. With respect to domestic water self-sufficiency, in this study, it was found that the RWHS could meet up to 80% of household demands. This result is similar to those reported by Farreny et al. (2013) from rooftops of Barcelona, ranging from 80% to 90% of water self-sufficiency. An important consideration not analyzed in this work is the size and location of the water tank, which play important roles regarding the feasibility of RWHS and environmental impacts (Angrill et al., 2012, 2017). Results showed an energy-sufficiency of 52% which is similar to the literature concerning potential roofs for integrating PV panels in Barcelona ranging from 30% to 46% (Riyahi Alam et al., 2008; Toboso-Chavero et al., 2018). 5. Conclusion This study contributes to defining criteria and procedures for assessing the feasibility of rooftops for integrating urban agriculture, rainwater harvesting systems, and PV panels. Data acquisition from remote sensing technology is the basis of the defined framework. In this sense, the availability of data is important. The identification of roof materials, can be estimated through the proposed method and the use of remote sensors in the present study. The lack of this information could be a limitation for 39 determining the potential adequate load capacity for the installation of greenhouses and could constitute an information gap in evaluating the potential integration and self-sufficiency of FWE systems in different cities. Daylight requirements for three categories of food production were established in this work with the aim of not excluding roofs that can be productive, even without high amounts of solar access. A lack of information concerning the consumption of products and production at the local or regional level could restrict or lead to less accurate production and self-sufficiency analyses. In relation to this limitation, experimental research on rooftop agriculture for both RTGs and open-air systems in different regions and considering a diversity of crops and according to their consumption products is needed. Urban morphology and its characteristics influence the feasibility of potential production, especially for food and energy. Thus, the relationship between urban morphology and building typologies regarding FWE systems must be performed more deeply. This issue could be addressed by characterizing urban structure types using remote sensing technology due to the potential for efficient derivation of mapping urban land at the city scale. It is important that information about potential roofs be accessible and easy to identify. In this regard, an interactive map with the location and information of these areas has already been implemented in some cities, for example, the rooftop project maps of Melbourne city (City of Melbourne, 2020). Developing this type of interactive map could represent a valuable contribution to decision making for planning. 40 The results of this work indicate that housing and services buildings could be a better location for RUA than industrial buildings. In addition, RUA could represent social cohesion and educational values more directly. Regarding water self-sufficiency for crop production, the case study demonstrated good performance for most of the systems studied. However, this performance is variable according to the rainwater amounts of the case study location. Some implications can be improved for future works, for example, considering the recirculation of leachate and the factor of occupied area by crops in the case of open-air systems, as well as with respect to water tanks, size calculations, and feasibility locations, which could be restrictions for implementing RWHS regarding the tank weight and associated environmental impacts. This work explored potential rooftops already built, but the integration of FWE systems into new buildings is also important. The expansion of cities continues, and the nexus of FWE systems should be considered a part of new buildings. In this sense, the integration of water and energy flows as well as UA in the phase of project design is an important consideration. For these reasons, it is crucial that FWE studies also focus on the development of new projects and designs that will strongly depend on the geographic location. Disclosure statement No potential conflicts of interest were reported by the authors. Acknowledgements This work is part of the Fertilecity II project supported by the Spanish Ministry of Economy and Competitiveness (CTM2016-75772-C3-3-R and CTM2016-75772-C3-1-R, AEI/FEDER, UE); from the Spanish Ministry of Science, Innovation and Universities. This research has received financial support from the European Union’s Horizon 2020 research and innovation program under grant agreement No 862663. The publication reflects the author’s views. The 41 Research Executive Agency (REA) is not liable for any use that may be made of the information contained therein. This work was supported by the “María de Maeztu” program for Units of Excellence in R&D (CEX2019-000940-M). Authors want to thank to Assumpció Termes and Fernando Pérez-Aragüés from The Institut Cartogràfic i Geològic de Catalunya for their valuable support, to the Greenhouses to reduce CO2 on Roofs (GROOF) project, to the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya for the award of a research scholarship (FI-DGR 2018) to Joan Muñoz-Liesa and to the University of Guadalajara (Mexico) for awarding a research scholarship to Perla Zambrano-Prado. Appendices Criteria to integrate FEW systems for commercial and non-profit purpose Phase 1: Study area characterization The characterization of the study area is based on the following criteria: a) Urban features. It is important to know the urban context, such as urban form, population density, use and typology of building. b) Climatic conditions. Resource potential is crucial to determine the feasibility for integrating food, water, energy (FEW) systems. Sunlight amount is necessary for growing food and determine suitable crops, directly affecting crop yields. Solar radiation reached by plants directly impacts the photosynthetically active radiation (PAR), which describes the generally accepted light wavelengths between 400 and 700 nm useful for photosynthesis. The relationship between solar radiation (normally measured in global horizontal radiation, in W/m2h) and PAR (measured with the Photosynthetic Photon Flux Density, in μmol/m2s) cannot be determined accurately unless values for each individual wavelength are known (Langhans & Tibbitts, 1997). Here, an approximation conversion value is calculated using global broad bandwidth and PAR measurements taken during the 2016-2018 period in the Barcelona area in order to give a more precise on-field data. The resulting average conversion factor from recorded daily solar radiation to PAR values used in this study was 1.79  0.01 (CI 95%), which contrasts with the 2.29 and 2.12 theoretically reached in daylight 42 and blue-sky conditions respectively (Thimijan & Heins, 1983), which is normally referred in literature. Thus, the 1.79 here used is a safety factor that eliminates over-prediction errors, which could deviate up to +28% the measured PAR value. This ables to calculate the cumulative measurement of total daily photons reached by plants in MJ/m2d (known as day light integral, DLI, normally expressed in μmol/m2s) in order to explain the ideal light requirements that saturate the leaf net photosynthetic rate to maximize plant growth (Kozai et al., 2015). Rainfall over the year plays a key role in determining whether RWHS can compete with other water supply systems, as a general rule, rainfall should be over 50 mm/month for at least half a year or 300 mm/year (FAO, 2014; Worm & van Hattum, 2006). 2.2.2 Phase 2: Urban and architectural requirements To identify suitable roofs, this phase considers criteria at urban and architectural scales. a) Urban. Planning and building laws and codes must be considered to ensure that the integration of FWE systems meet legal requirements. Cities are composed of diverse building types and a variety of uses, such as educational, housing, health, public services, commercial and industrial. Building use is important criterion that can define the purpose of the FWE systems. For example, educational buildings can integrate these systems for educational and self-sufficient purposes (Nadal et al., 2019), commercial and industrial buildings for commercial purposes (Nadal et al., 2017; Sanyé-mengual et al., 2015; Sanyé-Mengual et al., 2016) . Building uses are usually defined in urban planning and local ordinances. b) Architectural. The characterization of buildings roofs (area, slope, material, radiation and sunlight access) is needed to identify feasibility areas. 43  Roof area. A minimum roof area of 500 m2 is needed for integrating UA for commercial purposes (Nadal et al., 2017). A minimum roof area of 13 m2 can satisfy the vegetal demand for one person using soil less agriculture systems (Boneta et al., 2019) this was considered for non-profit purpose. Taking into account the energy consumption and the yearly operation and maintenance cost, photovoltaics (PV) suitability requires at least a minimum roof area of 100 m2 for commercial use and 24 m2 for residential use (Spertino, Di Leo, & Cocina, 2013). The established areas can be geographically sensitive due to major consumption typical at latitudes with less sunlight hours.  Roof slope. For integrate UA systems, slope of roofs must be flat ≤ 10% (Nadal et al., 2017). For rainwater harvesting systems (RWHSs) and PV systems, roof slope was not considering, assuming that PV panels are adjustable.  Solar radiation. Energy systems require solar radiation higher than 13 to 14 MJ/m2 per day (Nadal et al., 2017). The amount of radiation is essential for growing food, in this work was established three levels of day light requirements for crops. Daily light requirements (measured with DLI) vary depending on the crop and its photoperiod, which range between 6 to 35 mol/m2d. Current methodologies to identify potential urban agriculture areas normally define a target DLI value between 20 – 25 mol/m2d, originally expressed in MJ/day (Benis et al., 2017; Nadal et al., 2017; Sanyé-mengual et al., 2015). Other literature refers to the minimum average solar radiation of 8.5 MJ/m2d defined for Mediterranean crops as reported by Nisen et al. (1998). These DLI targets usually satisfy light requirements for multiple selected crops or single species like tomato plants, which is a reference crop for agriculture studies. However, these are high compared to the optimal DLI requirements for other valuable crops as microgreens (see Table 1), underestimating the urban potential area that could grow low and medium-DLI crop types. Here, three groups of crops are classified according 44 to their optimal daylight needs (i.e., maximum yields), even lower light levels will linearly decrease yields (Kozai et al., 2015) within acceptable crop yields and environmental performance (Rufí-Salís et al., 2020) Grouped DLI requirements are expressed in mol/m2 and in MJ/m2 daily and annually in order to facilitate comprehension and applicability of this workflow (see light conversion factor section). Hence, the approach here is to adapt the crop species to the available urban solar radiation, since adapting the light requirements with additional LED lightning would lead into an excess of energy needs with high environmental costs (Benis et al., 2017).  Roof material. Roof materials are essential data to integrate these systems, if a roof does not have the necessary load-bearing capacity to support the weight of the system, it will not be possible to install a system on it. A minimum load-bearing capacity 200 kg/m2 is required (Nadal et al., 2017). In the case of RWH, the quality and quantity of rainwater harvested from roofs are significantly affected by roofing materials (Farreny et al. 2011). Phase 3: Self-sufficiency indicators. Production of FEW systems must be calculated to obtain the production and the self-sufficiency potential. To do this, the following three steps are required: a) Resource production. Potential production is used as indicator to assess the FWE systems integration and to estimate the degree of self-sufficiency. Suitable crops must be identified based on household typical food diet, for this reason, suitable crops can be geographically sensitive. Irrigation water requirements of crops data are needed. Water 45 demand, depend on the type of crops and growing methods an systems, if this information is not available for the selected crop in and the geographic region of the study area, data of evapotranspiration (ETo, mm) is required to calculate (Khangaonkar & Mehaute, 1991). The following considerations should be taken to quantify resource production:  Food. To quantify crop yield values, growing method (RTGs or open-air) and growing system (soil less or soil-based) must be considered.  Water. Average rainfall data and runoff coefficient (RC) is required to estimate the potential rainwater harvesting on roofs. RC varies according to roof material from 0.9 to 0.6 (Farreny et al., 2013).  Energy (PV). The technology for PV for example the most commonly in the market. c) Self-sufficiency. For this phase, two criteria are necessary: potential production and household consumption. The potential food for self-sufficiency is calculated by dividing the potential production (food, water, energy) by the average consumption of the food (kg·person·year), water (lts·person·year) and energy (kWh·person·year) in the study area, resulting in the total number of people whose demand is satisfied. Table 1. Average yield values and water recquirmets for selected crops Growing Growing Crop type Crop growing Reference method System yield season (kg/m2) Sanjuan- Spring- RTGs soil-less Tomatoe 15.3 Delmás et al. summer (2018) Spring- Boneta et al. Open-air soil-less Tomatoe 5.8 summer (2019) 46 Spring and Rufí-Salís et RTGs soil-less Lettuce 8.36 autum al. (2020) Boneta et al. Open-air soil-less Lettuce 3.85* Anually (2019) *Lettuce crop yield value in open air considers a policultive of vegetables production. Production in RTGs considers passive heating. Table 2. Water requirement for tomatoes and lettuce irrigation according to growing systems. Growing Growing Crop type growing Water Reference method System season use (L/kg) Rufí-Salís et RTGs soil-less Tomatoe Spring-summer 66 al. (2020) Boneta et al. Open-air soil-less Tomatoe Spring-summer 130.5 (2019) Summer and 77.6 Rufí-Salís et RTGs soil-less Lettuce autumn al. (2020) Boneta et al. Open-air soil-less Lettuce Spring-summer 130.5 (2019) Light conversion factor Light conversion factor has been studied with on-field measurements in the Agricultural facility in the compounds of Universitat Politècnica de Catalunya in the city of Viladecans, in the Barcelona region. The weather station in this facility (see Figure A1) is equipped with a global solar radiation (Hukseflux LP02, second class pyranometer) and a PAR Quantum 47 sensor (SKP215 with a ±5% accuracy, typically <±3%) which measures incident quanta between 400 and 700 nm. A CX1000 datalogger from Campbell Scientific measures data every 5sec, recording the averages at hourly intervals during the 2016 to 2018 period. Figure 1. Weather station facility in the country-side area of Viladecans, Barcelona. © Universitat Politècnica de Catalunya In order to study the Day Light Integral of crops, the sum of all incident radiation of both sensors has been summed per day and then divided in order to calculate the conversion factor from global radiation (W/m2) to PAR (PPFD, in μmol/m2s). The obtained values in a box plot are presented in detail in Figure 2. Note that significant differences exist between year quarters, being the second and third quarter greater than the first and fourth. However, as the assessment here is annual based, a unique average value of 1.79  0.14 for all year has been used. 48 Grouped DLI requirements are expressed in mol/m2 and in MJ/m2 daily and annually in order to facilitate comprehension and applicability of this workflow. It is important to note that vegetative crops do not require significant lower DLI during the early stage of the plants development, while fruiting crops like tomatoes require significant lower levels (from 2 to 13- 20 mol/m2d) during the vegetative growth stage compared to the fruiting phase up to 40 mol/m2d (Schwarz et al., 2014). Therefore, when calculating the annual light needs for tomato plants in the high DLI group, an average value between 20 and 25 mol/m2d is assumed (i.e., considering that the vegetative phase lasts the same period as the fruiting phase (Philips Lighting Horticulture, unpublished work). The growing period has been considered according to the average months in the Mediterranean area, excluding the coldest months (December and January) (FAO, 2013). Similarly, one short tomato crop lasting 6 months has been also considered according to common practices in the Mediterranean climate while an extended tomato crop of 8 months has been considered for rooftop greenhouse farming. Finally, an average solar transmissivity radiation of 70% has been considered, even this could vary according to roof slope and orientation (Castilla, 2005). Table 3. Crop classification according to DLI requirements. *Seedling is here reported but not considered when stablishing DLI groups. Day light requirements MJ/m2·year Optimal DLI Reference Crop specie (DLI target) OA / RTG (mol/m2·day) Kozai et al. Low > 6 mol/m2 > 1000* (2015) Microgreens 6—12 > 3.4 MJ/m2 > 1700* Verlinden (2020) Small vegetative Kozai et al. < 12 crops, green shoots (2015) 49 Albright et al. Lettuce 12—17 Medium > 12 mol/m2 > 2000* (2000) Albright et al. > 6.7 MJ/m2 > 3500* Leafy crops 12—17 (2000) Fan et al. Vegetable seedlings 13 (2013) Kozai et al. Strawberries 13 (2015) Young / low-wire Ingestad et al. 13—17.3 tomato (e.g. cherry) (1994) Avg 22—26 Spaargaren High > 2200** High-wire tomato > 22 mol/m2 Up to 30—35 (2001) > 4700*** >12.3 MJ/m2 Avg 22—29 Schwarz et al. High-wire tomato Up to 30—40 (2014) * Considering 10 and 12 months of crop season for OA and RTG farming, respectively. ** Considering 6 months of crop season equivalent to one short tomato crop. *** Considering 8 months of crop season equivalent to one extended tomato crop. 50 2,4 2,4 2,3 2,3 2,2 2,2 2,1 2,1 2,0 2,0 1,9 1,9 W/m² t o PAR [conver sion f act or ] W/m² t o PAR [conversion f act or] 1,8 1,8 1,7 1,7 1,6 1,6 1,5 1,5 1,4 1,4 1,3 1,3 Quart er 1,2 1,2 Q1 Q2 Q3 1,1 1,1 Q4 1,0 1,0 Q1 Q2 Q3 Q4 Figure A2. Annual and quarter conversion factor values. Appendices References Albright, L. D., Both, A. J., & Chiu, A. J. (2000). Controlling greenhouse light to a consistent daily integral. Transactions of the American Society of Agricultural Engineers. 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