Are Biocrusts and Xerophytic Vegetation a Viable Green Roof Typology in a Mediterranean Climate? A Comparison between Differently Vegetated Green Roofs in Water Runoff and Water Quality

water Article Are Biocrusts and Xerophytic Vegetation a Viable Green Roof Typology in a Mediterranean Climate? A Comparison between Differently Vegetated Green Roofs in Water Runoff and Water Quality Bernardo Rocha 1,* , Teresa A. Paço 2,3 , Ana Catarina Luz 4, Paulo Palha 5, Sarah Milliken 6, Benzion Kotzen 6, Cristina Branquinho 1, Pedro Pinho 1and Ricardo Cruz de Carvalho 1,7   Citation: Rocha, B.; Paço, T.A.; Luz, A.C.; Palha, P.; Milliken, S.; Kotzen, B.; Branquinho, C.; Pinho, P.; de Carvalho, R.C. Are Biocrusts and Xerophytic Vegetation a Viable Green Roof Typology in a Mediterranean Climate? A Comparison between Differently Vegetated Green Roofs in Water Runoff and Water Quality. Water 2021,13, 94. https://doi.org/ 10.3390/w13010094 Received: 13 November 2020 Accepted: 28 December 2020 Published: 4 January 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1cE3c, Centre for Ecology, Evolution and Environmental Changes, Faculty of Sciences, University of Lisbon, Campo Grande, Edifício C2, Piso 5, 1749-016 Lisbon, Portugal; [email protected] (C.B.); [email protected] (P.P.); [email protected] (R.C.d.C.) 2Department of Biosystems Engineering, Institute of Agronomy, University of Lisbon, Tapada da Ajuda, 1349-017 Lisbon, Portugal; [email protected] 3 Linking Landscape, Environment, Agriculture and Food (LEAF), Institute of Agronomy, University of Lisbon, Tapada de Ajuda, 1349-017 Lisbon, Portugal 4ISEG—Instituto Superior de Economia e Gestão da Universidade de Lisboa, Rua do Quelhas 6, 1200-781 Lisboa, Portugal; [email protected] 5Neoturf, Rua das Amoreiras 155, 4460-227 Senhora da Hora, Portugal; [email protected] 6School of Design, University of Greenwich, Park Row, London SE10 9LS, UK; [email protected] (S.M.); [email protected] (B.K.) 7MARE—Marine and Environmental Sciences Centre, Faculty of Sciences, University of Lisbon, Campo Grande, Edifício C2, Piso 5, 1749-016 Lisbon, Portugal *Correspondence: br[email protected]; Tel.: +351-916-108-006 Abstract: Green roofs can be an innovative and effective way of mitigating the environmental impact of urbanization by providing several important ecosystem services. However, it is known that the performance of green roofs varies depending on the type of vegetation and, in drier climates, without resorting to irrigation, these are limited to xerophytic plant species and biocrusts. The aim of this research was therefore to compare differently vegetated green roofs planted with this type of vegetation. A particular focus was their ability to hold water during intense stormwater events and also the quality of the harvested rainwater. Six test beds with different vegetation compositions were used on the roof of a building in Lisbon. Regarding stormwater retention, the results varied depending on the composition of the vegetation and the season. As for water quality, almost all the parameters tested were higher than the Drinking Water Directive from the European Union (EU) and Word Health Organization (WHO) guidelines for drinking-water quality standards for potable water. Based on our results, biocrusts and xerophytic vegetation are a viable green roof typology for slowing runoff during stormwater events. Keywords: nature-based solutions; ecosystem services; sustainability; stormwater retention; water reuse; Mediterranean climate; biocrust roofs; xerophytic vegetation 1. Introduction Many urban areas have been steadily increasing both in size and density as more people migrate from rural areas [ 1 ]. Increasing population densities in urban environments can lead to an intensification of the urban heat island effect [ 2 ] and higher vulnerability to flooding, as settlements on floodplains, deforestation, land conversion and an increase in impervious surfaces areas can increase the number of flood events and their associated risks [ 3 – 5 ]. In fact, the Mediterranean basin experiences several flood episodes every year, mainly during the autumn and winter months [ 6 , 7 ], namely in southeast Spain, southern Water 2021,13, 94. https://doi.org/10.3390/w13010094 https://www.mdpi.com/journal/water Water 2021,13, 94 2 of 19 France, Italy, Greece and Israel [ 6 – 8 ]. With climate change set to intensify this trend in the future, not only will such episodes become more frequent, but also more people will be vulnerable to them, increasing the risk of health hazards [ 5 , 9 – 14 ] and significant economic losses [ 9 , 12 ]. The reduction in biodiversity due to habitat fragmentation and/or degradation [ 15 – 18 ] and the increase in pollution levels [ 19 – 21 ] are two other major threats that can arise or be aggravated by higher population densities in urban environments. One of the solutions to counter these threats is to create more and improve existing, green spaces. These not only cool the atmosphere due to the evapotranspiration process [ 22 – 24 ] but also can be used both to regulate the urban water cycle by reducing the amount of stormwater runoff and to improve water quality by removing pollutants from runoff. Vegetated streetscapes designed to absorb water, such as bioswales and rain gardens, have been shown to be particularly effective, and while street trees intercept rainfall in their canopies and store water on their leaves and stems until it is subsequently evaporated, those planted in tree pits considerably increase the infiltration rate and thereby reduce surface water runoff [ 25 ]. However, the rise in the demand for more housing and industry in urban areas tends to prevent the expansion and preservation of green areas. A possible solution to compensate for these issues is the use of green roofs, which are classified as intensive, semi-intensive or extensive, depending on the depth of the substrate. An extensive green roof type has a shallow substrate, small plants, and low maintenance requirements, usually without irrigation, which makes it more suitable for implementation at a large scale. In contrast, an intensive green roof is characterized by a deeper substrate, taller vegetation varieties, and therefore higher maintenance and usually more advanced irrigation systems. Green roofs have the potential to offer a wide range of benefits [ 26 – 28 ], such as: (i) stormwater management [ 29 ], since they absorb and hold rainfall, thereby preventing or at least mitigating flooding episodes, with the possibility of reusing the water retained for irrigation [ 30 ]; (ii) increase building insulation, keeping it warmer in the winter and cooler during summer, thereby reducing energy consumption [ 26 , 31 ]; (iii) mitigating the urban heat island effect [ 32 , 33 ]; (iv) sequestering air pollutants such as CO 2 [ 34 ]; and (v) creating habitats for flora and fauna, mainly insects and birds [ 35 – 37 ]. They may also be partially noise absorptive. Extensive research has been undertaken on these benefits in comparison to more traditional roof materials. Several studies have already assessed how green roofs perform concerning stormwater retention [ 38 – 43 ], not only when compared to traditional roofs but also comparing different types of green roof vegetation. Green roofs may delay the timing of peak runoff, thereby alleviating stress on storm-sewer systems, by storing water in the growing medium and to a lesser extent in the vegetation canopy. The ability of a roof to retain stormwater depends on factors such as the intensity and duration of the rain event, the drainage element of the roof, as well as substrate depth and composition, substrate moisture content at the start of the rain event, and the type, health, and density of the vegetation [25]. In a Mediterranean climate, extensive green roof vegetation is limited to species able to endure a dry and hot environment, high solar exposure, and wind. One way to extend the range of plants would be to use irrigation systems, a solution already used on most green roofs, particularly during the summer season. However, this solution is neither feasible on a large scale, as it would be very costly, nor is it sustainable. Therefore, innovative ideas need to be explored to enable more widespread use of green roofs in southern Europe. The NativeScapeGR project [ 44 – 46 ], conducted in the city of Lisbon, tested the idea of using various native vascular plants and mosses, which are well adapted to the Mediterranean climate (http://www.isa.utl.pt/proj/NativeScapeGR/). The results showed that different species were able to survive summer harsh conditions by only using small irrigation volumes [ 46 ]. Nevertheless, it is important to note that this innovative typology for green roofs for hot, dry climates may not be as efficient as other types of plants in terms of the ecosystem services they provide. Therefore, a study was carried out to assess how differently vegetated green roofs perform concerning episodes of intense stormwater runoff, by simulating intense rainfall events. Additionally, tests were conducted on the Water 2021,13, 94 3 of 19 concentration of several key parameters of the runoff water to ascertain whether it meets European and international potable water standards. 2. Materials and Methods 2.1. Study Area The fieldwork was conducted from February to October 2019 on the roof of the Herbarium building (38.707992, − 9.184544) at the Instituto Superior de Agronomia (ISA) (Figure 1) on the outskirts of the city of Lisbon, Portugal. The surrounding landscape consists of farmland and urban areas composed of housing and roads. Figure 1. Location of the Instituto Superior de Agronomia, Lisbon, Portugal. Test beds were installed on the roof of a building in the Instituto complex. The yellow triangle marks the precise location of the test beds. For this study, six of the twelve metal test beds that had been constructed for the 2014 NativeScapeGR project [ 44 ] were used (Figure 2). The test beds (2.5 × 1 × 0.2 m) are elevated 1 m above the roof surface and have a slight slope of 2.5% to facilitate rainwater drainage towards a small drainage hole in the top right corner of the test bed. The test beds were designed to mimic an extensive green roof. They consist of a bottom layer of geotextile, followed by a 25 mm polyethylene drainage layer with cavities for water storage with an effective storage depth of 3 L/m 2 or 3 mm and a filter. The upper layer consists of a commercial green roof substrate, with 71% organic matter content (texture not classified due to high organic matter content) mixed with a medium to coarse texture LECA (Lightweight Expanded Clay Aggregate) with a 2 to 4 mm thickness, corresponding to approximately 30% of the composition of the substrate layer. Water 2021,13, 94 4 of 19 Figure 2. Metal test beds (2.5 × 1 × 0.2 m). The test beds used in this study are marked in green. From ( A – F ): ( A ) test bed #3; ( B ) test bed #5; ( C ) test bed #6, ( D ) test bed #10, ( E ) test bed #11 and ( F ) test bed #12 (photographs taken on the 20 May 2019). 2.2. Vegetation Composition, Functional Traits and Cover Although the test beds had already been used in previous studies [ 44 – 46 ] and therefore undergone different substrate and vegetation compositions, since the end of 2018 they had all been filled with a commercial green roof substrate, with 71% organic matter content. Since the end of 2018 [ 44 , 45 ], the vegetation composition within each test bed has been a combination of four species that were planted during a previous project (2014–2017) [ 44 ]— Dittrichia viscosa (L.) Greuter, Lavandula stoechas subsp. luisieri (Rozeira) Rozeira, Pleurochaete squarrosa (Brid.) Lindb. and Sedum sediforme (Jacq.) Pau—and nine species that naturally colonized the test beds—Briza maxima L., Conyza sp., Digitaria sanguinalis (L.) Scop., Filago pyramidata L., Gomphocarpus fruticosus (L.) W.T.Aiton, Illecebrum verticillatum L., Teucrium scorodonia L., Trifolium angustifolium L. and Vulpia geniculata (L.) Link. Species composition is shown in Table 1and their functional attributes in Table 2. Test bed #3 had the highest species richness with a total of eight species, followed by test bed #5 with seven species, test bed #6 with six species, test bed #10 with four species and lastly test beds #11 and #12 with, respectively, three and one species. Filago pyramidata and Sedum sediforme were the most common species, being present in four of the six test beds. In contrast, Digitaria sanguinalis, Gomphocarpus fruticosus,Illecebrum verticillatum and Lavandula stoechas var. luisieri were present in only one of the test beds (Table 1). Water 2021,13, 94 5 of 19 Table 1. Plant composition, taxonomic diversity and functional diversity regarding plant life form and root type. Species Test Bed #3 Test Bed #5 Test Bed #6 Test Bed #10 Test Bed #11 Test Bed #12 Briza maxima • • Conyza sp. • • • Digitaria sanguinalis • Dittrichia viscosa • • Filago pyramidata • • • • Gomphocarpus fruticosus • Illecebrum verticillatum • Lavandula stoechas subsp. luisieri • Pleurochaete squarrosa • • • Sedum sediforme • • • • Teucrium scorodonia • • Trifolium angustifolium • • Vulpia geniculata • • • Total number of species 8 7 6 4 3 1 Functional diversity 19 18 17 16 10 4 Life form diversity 5 3 3 4 2 1 Functional diversity (Table 1), considering all traits seen in Table 2, was higher in test bed #3, #5, #6 and #10, and lower in test bed #11 and #12. Life form diversity (Table 1) was also higher in test bed #3, which had at least one specimen of each life form functional group, except for tall shrubs, followed by test bed #10, test bed #5 and #6, and lower in test bed #12, with only a moss species present. The amount of vegetation cover also varied among the test beds (Figure 2). Vegetation cover was determined based on vertical photographs taken of each test bed and refers to the area of the test bed surface covered by vegetation. Test beds #5 and #11 had the highest amount of vegetation cover, both with approximately 80% cover. They were followed by test bed #10, with approximately 40%. Test bed #3 and #6 had a similar amount of cover, with approximately 25%. Test bed #12 had the lowest amount, with approximately 15%. 2.3. Methodologies Used to Assess Ecosystem Services 2.3.1. Stormwater Management Tests on the duration and volume of stormwater runoff were conducted on three different occasions in 2019, one before summer (21 of May) and two after (4 and 24 of October). All six test beds were irrigated with 40 L of water from the municipal drinking water system during 135 s to simulate a very extreme episode of flash flooding, as this corresponds to approximately 427 mm/h. It is important to note that this value was chosen as the substrate in all test beds was very dry by the time of the tests. For that reason, a very high irrigation volume was used to ensure that significant runoff differences between test beds would be produced and avoid the majority of the irrigated water being absorbed and not runoff. Irrigation was conducted by placing a hose vertically above the test bed and using a flow control water meter (NATRAIN NWC) to keep track of the volume of water. A vertical irrigation process was established to mimic, to the best extent possible, a normal rainwater pattern, which mainly falls vertically or slightly tilted. To ensure that the test beds surface was evenly irrigated, the hose was placed approximately 1.70 m above the test beds surface. Irrigation was immediately stopped after reaching the desired irrigation volume (40 L). Runoff time was measured using a chronometer that initiated counting when irrigation started and stopped when the water started to pour out of the drainage hole. Runoff volume was measured using several 5 L plastic buckets. No surface runoff occurred as the height of the metal frame of the test bed was always, at least, 10 cm higher than the substrate level. Water 2021,13, 94 6 of 19 Table 2. List of species present in the test beds and their respective functional characterization. Stem height classes: small (0 to <20 cm), medium (20 to 100 cm) and tall (>100 cm). Stem height for each species is given in centimeters (cm) and refers to the average height found in the literature. All functional information was gathered from extensive online research. Species Average Stem Height Canopy Density Life Form N Fixation Hydric Regulation Life Cycle Root Type Photosynthetic Pathway Exotic/Native Briza maxima Medium (80 cm) Low Grass No Homoiohydric Annual Fibrous root C3 Native Conyza sp. Tall (120 cm) Medium Forb No Homoiohydric Annual Taproot C3 Exotic Digitaria sanguinalis Medium (60 cm) Low Grass No Homoiohydric Annual Fibrous root C4 Exotic Ditrichia viscosa Tall (130 cm) Medium Shrub No Homoiohydric Perennial Taproot C3 Native Filago pyramidata Medium (35 cm) Low Forb No Homoiohydric Annual Taproot C3 Native Gomphocarpus fruticosus Tall (200 cm) High Tall shrub No Homoiohydric Perennial Taproot C3 Exotic Illecebrum verticillatum Small (2 cm) Mat-forming Forb No Homoiohydric Annual Taproot C3 Native Lavandula stoechas var. luisieri Medium (60 cm) Medium Shrub No Homoiohydric Perennial Fibrous root C3 Native Pleurochaete squarrosa Small (2 cm) Mat-forming Moss No Poikilohydric - - C3 Native Sedum sediforme Medium (60 cm) Mat-forming Succulent No Homoiohydric Perennial Taproot CAM Native Teucrium scorodonia Medium (50 cm) Medium Forb No Homoiohydric Annual Fibrous root C3 Native Trifolium angustifolium Medium (50 cm) Low Forb Yes Homoiohydric Annual Taproot C3 Native Vulpia geniculata Medium (60 cm) low Grass No Homoiohydric Annual Fibrous root C3 Native Water 2021,13, 94 7 of 19 2.3.2. Runoff Quality Runoff water for the quality tests was collected on the 21 May 2019. A volume of 40 mL of runoff water was collected in plastic sample collection containers (50 mL), labelled, and taken to the laboratory. During the test, water pooled on the surface of the test beds. Water that infiltrated faster was potentially less contaminated that the one who pooled, as the later had more time to absorb contaminants, present in the substrate surface, before infiltrating to deeper layers of the test bed. For that reason, only the initial runoff was collected, which corresponds to the water that infiltrates immediately instead of pooling. We also collected water from the municipal drinking water system and used it as a control. In the laboratory, each of the seven water samples, one for each test bed plus the control, was tested for ammonia and ammonium (NH 3 ; NH 4+ ), nitrate (NO 3 − ) and phosphate (PO 43− ) concentrations using an Aquaculture Photometer HI8339 (Hanna Instruments, UK) and following the procedures recommended in its instructions. 2.4. Statistical Analysis All runoff times and volume values, as well as the water parameter concentration values, were stored in a database using Microsoft Excel, version 2010 [ 47 ]. Averages runoff time and volume were calculated for each of the six test beds and each of the three runoff tests. Runoff coefficient, per test bed, was calculated by dividing the average volume of water runoff (mean of the three tests) by the irrigation volume (40 L). Plant functional diversity (Table 1) was calculated based on the number of functional groups from all traits (Table 2) present in each test bed and was included as a variable and related to the runoff performance of the test beds. Plant life form diversity derives from the total functional diversity. For each response variable considered in this study, differences between the test beds were evaluated with a one-way ANOVA with Tukey’s multiple comparisons test, using GraphPad Prism 6.03 for Windows (GraphPad Software, San Diego, CA, USA) [48]. 3. Results 3.1. Stormwater Management Runoff time and volume varied both among test beds and across the three different tests (Tables 3and 4; Figures 3–7). The average of the three runoff tests showed that higher runoff volumes were accompanied by lower runoff times (Figure 3). Table 3. Measurements of all test bed runoff times, in seconds, for test 1, 2 and 3. The bottom line represents the average runoff time measured across all test beds, for each test. The last column represents the average time average runoff time measured across all tests, for each test bed. Runoff Time (s) Test nr. 1 (21 May) Test nr. 2 (4 October) Test nr. 3 (24 October) Test Bed Average Test bed #3 110 64 136 103 Test bed #5 118 101 137 119 Test bed #6 125 116 119 120 Test bed #10 134 116 150 134 Test bed #11 140 64 123 109 Test bed #12 107 57 127 97 Test Average 122.3 86.3 132.0 Water 2021,13, 94 8 of 19 Table 4. Measurements of all test bed runoff volumes, in liters for the three distinct test periods (Test nr. 1, 2 and 3) . The bottom line represents the average runoff volume measured across all test beds, for each test. The antepenultimate column represents the average runoff volume measured across all tests, for each test bed. The last column represents the runoff coefficient, calculated for each test bed, based on their test bed averages. Runoff Volume (L) Test nr. 1 (21 May) Test nr. 2 (4 October) Test nr. 3 (24 October) Test Bed Average Runoff Coefficient Test bed #3 9.65 15 13.75 12.80 0.32 Test bed #5 9.75 11.7 14 11.82 0.30 Test bed #6 6.5 13.5 15.5 11.83 0.30 Test bed #10 5.9 10 15 10.3 0.26 Test bed #11 7.6 21.5 20 16.37 0.41 Test bed #12 10.6 22.5 18 17.03 0.43 Test Average 8.3 15.7 16.0 Figure 3. Average runoff time and volume values registered during the three distinct test periods across all test beds. Whiskers represent the standard deviation. Runoff volume is expressed in liters (L). Runoff time is expressed in seconds (s). Figure 4. Average runoff time and volume values registered during the three test periods. Letters (a) and (b) represent statistical differences between variables (tests). Whiskers represent the minimum and maximum values. Runoff volume is expressed in liters (L). Runoff time is expressed in seconds (s). Water 2021,13, 94 9 of 19 Figure 5. Runoff time values registered in the three distinct test periods across all test beds. Runoff time values expressed in seconds (s). Figure 6. Runoff volume in the three distinct test periods across all test beds. Runoff volume values expressed in liters (L). Figure 7. Average runoff time in each of the six test beds (on the left). Average runoff volume values registered in each of the six test beds (on the right). Whiskers represent the standard deviation. Runoff time values expressed in seconds (s), runoff volume values expressed in liters (L). The one-way ANOVA test allowed for comparison to be made between the average values registered across the three test periods (Figure 4). Regarding the runoff time, Water 2021,13, 94 16 of 19 noted between the test beds demonstrate that species selection, in terms of functional and life form diversity, can further enhance this ecosystem service, since a combination of different vegetation functional characteristics leads to increased water retention. Runoff water quality also varied across the test beds, but the results did not show a link between parameter concentrations and vegetation composition and cover, which suggests that the substrate composition has the greatest impact on green roof performance regarding rainwater quality. Overall, we showed that biocrusts and xerophytic vegetation species composition, when chosen carefully, are suitable for green roofs in Mediterranean areas, as they can delay runoff during intense rainfall events. Supplementary Materials: The following are available online at https://www.mdpi.com/2073-444 1/13/1/94/s1, Table S1: Weather data from the meteorological station of Tapada da Ajuda, distanced approximately 200 m from our study area (38.7095611, − 9.18282500) for April and May 2019. Mean T, Max T and Min T represent the daily mean, maximum and minimum temperature recorded at 1.5 m above ground. Values expressed in tenths of ◦ C. Grass T represent the daily minimum temperature recorded at 1.5 cm above the ground. Values expressed in tenths of ◦ C. Soil mean T, Soil max T and Soil min T represent the daily mean, maximum and minimum temperature recorded at 10 cm below ground. Values expressed in tenths of ◦ C. Mean H, Max H and Min H represent the daily mean, maximum and minimum air relative humidity at 1.5 m above ground. Values expressed in %. Precipitation represents total daily precipitation. Values expressed in tenths of mm. Author Contributions: Conceptualization, R.C.d.C.; Data curation, B.R.; Formal analysis, B.R.; Investigation, B.R. and R.C.d.C.; Methodology, B.R. and R.C.d.C.; Project administration, R.C.d.C.; Writing—original draft, B.R.; Writing—review and editing, B.R., T.A.P., A.C.L., P.P. (Paulo Palha), S.M., B.K., C.B., P.P. (Pedro Pinho) and R.C.d.C. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Fundação para a Ciência e a Tecnologia (FCT) via the project MedMossRoofs (PTDC/ATP-ARP/5826/2014) and the research unit LEAF—Linking Landscape, Environment, Agriculture and Food (UID/AGR/04129/LEAF/2020). ACL was funded by FCT fellowship SFRH/BD/141822/2018. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: The authors wish to thank César Garcia (Faculty of Sciences of the University of Lisbon), for help with the identification of the bryophyte species. 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