ERI: CAS- Climate: Improving green roof technologies by modeling species-specific impacts in the urban microclimate

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。随着绿色屋顶的好处越来越明显，地方政府越来越多地开始强制或鼓励在新建筑中使用绿色屋顶。绿色屋顶已被证明可以增加雨水保留，降低冷却成本，减轻城市热岛效应，但这些效果在很大程度上取决于实施细节。例如，某些光合肉质植物物种，如景天属，通常选择用于绿色屋顶，因为它们的弹性和低维护要求。然而，这些物种的水分利用效率比典型的草本植物高出6至10倍，导致屋顶温度和径流率发生显着变化。一个关键的考虑植被的选择，基板，和维护策略，需要推进我们对这些技术的理解。研究人员试图建立在植物建模的最新进展，以准确地预测水的使用和生态系统服务的绿色屋顶方案作为植被类型的函数，并在各种环境条件下。建模工作将使用从绿色屋顶监测站点收集的数据，包括在波特兰州立大学校园进行的花园绿色屋顶实验。预计这项工作将导致模拟方法的绿色屋顶冷却和径流的功能，植被类型，气候条件和基板depth.While最近的实验工作已经显示出显着的影响植被类型的绿色屋顶生态系统服务，这些影响的机制还没有得到彻底的评估，使其难以概括的结果，以不同的气候和植被类型。这项工作将提高理解，并有能力模拟，植被对绿色屋顶冷却系统的影响。结果还将提高模型放大的理解，考虑到固有的非线性响应水分，温度和光照条件。这项工作将有助于开发工具，用于评估绿色屋顶环境中各种植被类型的适当性，作为主要气候条件的函数，有助于制定绿色屋顶政策指导方针和评估适当的绿色屋顶奖励措施。在波特兰州立大学校园的绿色屋顶网站收集的数据将在大学网站上公开。结合土壤-植物-大气连续体的开源Photo 3模型，这些数据将用于在波特兰州立大学的一个新的生态水文学课程中开发易于使用的动手模块。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). As the benefits of green roofs become clearer, local governments are increasingly beginning to mandate or incentivize their use in new construction. Green roofs have been shown to increase stormwater retention, lower cooling costs, and mitigate the urban heat island effect, but these effects depend strongly on the implementation details. For example, certain photosynthetic succulent species, such as Sedum sp., are commonly selected for use on green roofs because of their resiliency and low maintenance requirements. However, the water use efficiency of these species ranges from six to ten times higher than that of typical herbaceous plants, leading to significant changes in rooftop temperature and runoff rates. A critical consideration of vegetation choice, substrate, and maintenance strategies is needed to advance our understanding of these technologies. The investigator seeks to build upon recent advances in plant modeling to accurately predict water use and ecosystem services of green roof scenarios as a function of vegetation type and under a variety of environmental conditions. The modelling efforts will be informed using data collected from green roof monitoring sites, including a garden green roof experiment on the Portland State University campus. It is anticipated that the work will result in modeling approaches for green roof cooling and runoff as a function of vegetation type, climate conditions, and substrate depth.While recent experimental work has shown significant impacts of vegetation type on green roof ecosystem services, the mechanisms of these impacts have not been thoroughly evaluated, making it difficult to generalize the results to different climates and vegetation types. This work will improve understating of, and ability to model, vegetation impacts on green roof cooling in a systematic manner. Results will also improve understanding of model upscaling, taking into account the inherently nonlinear response to moisture, temperature, and light conditions. This work will enable the development of tools for evaluating the appropriateness of various vegetation types in green roof settings as a function of prevailing climate conditions, aiding in the formulation of green roof policy guidelines and the valuation of appropriate green roof incentives. Data collected at the green roof site on the Portland State University campus will be made publicly accessible on a university website. In conjunction with the open-source Photo3 model of the soil-plant-atmosphere continuum, the data will be employed to develop easy-to-use, hands-on modules in a new Ecohydrology class at Portland State University. The combined data and model will be packaged together in Google Colaboratory as a teaching tool for the engineering community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.