Due to their cooling ability, sustainable roofing configurations, such as green and cool roofs, have the potential to increase solar panel yield, which is temperature dependent. However, the influence of sustainable roofing configurations on panel yield is not yet considered in rooftop photovoltaic (PV) planning models; thus, the significance of these integrated systems cannot be evaluated. In order to quantify the potential benefits of sustainable rooftops on solar energy, the first goal of this study is to develop a method that systematically accounts for roof surface characteristics in the simulation of PV panel energy yield. To do so, a rooftop energy balance model is linked with a physically-based solar energy model (the System Advisor Model, SAM) to quantify the energy yield of PV installations on sustainable roofing configurations. Roof surface temperatures are first estimated using non-linear energy balance equations, then integrated into a revised version of SAM to simulate energy yield. This new method improves the accuracy of PV yield simulations, compared to prior assumptions of roof surface temperature equal to ambient temperature. This updated model is used for the second goal of the study, to understand how four roofing configurations (black membrane, rock ballasted, white membrane, and vegetated) influence PV panel yield, which is currently not well understood in cooler climates. For a flat rooftop PV installation near Zurich, Switzerland (temperate climate), results show that, compared to a conventional roof, green roofs can increase annual PV energy yield, on average, by 1.8%, whereas cool roofs can increase it by 3.4%. For the case-study installation, an inverse correlation between the 95th-quantile roof surface temperature and the PV energy yield was identified; an increase of 1 °C leads to a 71 kWh reduction in energy yield per year. Overall, cool roofs outperform green roofs in terms of increases in PV energy yield; however, potential improvements of both systems are non-negligible, even in relatively cooler climate regions like Switzerland. By providing a systematic method to evaluate the influence of the roofing configuration on PV energy yield, solar energy planners are able to differentiate between the benefits of traditional and sustainable rooftop configurations - the first step towards the coupling of distributed energy and sustainable building systems. In the future, this integrated method could be used as part of a holistic evaluation of the environmental, economic, and social objectives of green and cool roofs, as well as, other infrastructure systems.

Green roofs have the potential to offer numerous ecosystem services; however, they are rarely designed to achieve them. Instead, design is restricted by perceived structural and maintenance constraints, which consequently diminish the achievable benefits. For green roofs to improve sustainability and resilience of cities, their design should match their promised multi-functional application using performance-based design. The first step towards a comprehensive performance model is to synthesize design recommendations across disciplines to identify synergies and trade-offs in design objectives for multiple benefits. This study discusses design strategies that could alter the energy and water balance in the green roof in order to attenuate urban stormwater, increase building energy performance, mitigate urban heat, and improve the output of solar panels placed on top of green roofs. These benefits are mathematically linked to quantifiable processes (discharge rate, water content, evapotranspiration, sensible heat, net radiation, insulation, and thermal mass), forming the foundation for a performance-based design model. Design recommendations are then summarized for each process, followed by a discussion of synergies, trade-offs, and research needs that arise when green roofs are designed to achieve multiple functions. Selecting vegetation with high leaf area and albedo improves multiple benefits without affecting structural constraints, whereas choosing plants with low stomatal resistance leads to trade-offs between higher evapotranspiration and higher irrigation requirements. Trade-offs in substrate depth and properties including organic matter and moisture are also apparent. Interdisciplinary collaborations are needed to simulate and optimize design parameters based on stakeholder preferences related to co-benefits and constraints.

Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climate-corrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan.

As climate change alters precipitation patterns, stakeholders will need to understand how performance of green stormwater infrastructure (GSI) could change in response. As an alternative to using on-site monitoring, which may not always feasible, we propose that changes in performance could be tracked using annual rainfall measures (e.g., maximum daily rainfall per year). We estimated performance of GSI in 17 U.S. cities using rainfall measures by establishing linear relationships with specific performance metrics (e.g., frequency of discharge). Prediction accuracy was evaluated in 2 cities for the period 2020 to 2060 by comparing performance predicted from rainfall trends from regional climate models (RCMs) with simulated performance in SWMM using the same RCMs as input. Findings suggest that tracking rainfall measures can provide insight into the hydrologic performance of green infrastructure by predicting the direction of change, as well as, the magnitude within 25% to 50% percent change.