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

ERI：CAS-气候：通过模拟城市微气候中特定物种的影响来改进绿色屋顶技术

• 批准号: 2139003
• 负责人: Samantha Hartzell
• 金额: $ 19.34万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2139003&HistoricalAwards=false
• 关键词: ERI; CAS; Climate; Improving; green

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.

该奖项是根据2021年《美国救援计划法》（公法117-2）全部或部分资助的。随着绿色屋顶的好处变得更加清晰，地方政府越来越开始授权或激励其在新建筑中的使用。已显示绿色屋顶可以增加雨水的保留率，降低冷却成本并减轻城市热岛的效果，但这些效果在很大程度上取决于实施细节。例如，由于其弹性和较低的维护要求，通常选择某些光合肉质物种，例如阳台sp。。但是，这些物种的用水效率范围是典型草本植物的六到十倍，从而导致屋顶温度和径流速率发生显着变化。需要对植被，基材和维护策略进行批判考虑，以促进我们对这些技术的理解。研究人员试图基于植物建模的最新进展，以准确预测绿色屋顶情景的用水量和生态系统服务，这是植被类型的函数以及在各种环境条件下。建模工作将使用从绿色屋顶监测站点收集的数据，包括波特兰州立大学校园的花园绿色屋顶实验。预计这项工作将导致绿色屋顶冷却和径流的建模方法，这是植被类型，气候条件和底物深度的函数。尽管最近的实验性工作显示植被类型对绿色屋顶生态系统服务的重大影响，但这些影响的机制尚未得到彻底评估，因此很难将结果概括为不同的气和植被类型。这项工作将改善对植被对绿色屋顶冷却的影响，以系统的方式对植被的影响。考虑到对水分，温度和光条件的固有非线性反应，结果还将提高对模型升级的理解。这项工作将能够开发工具，以评估绿色屋顶环境中各种植被类型的适当性，这是盛行的气候条件的函数，有助于制定绿色屋顶政策指南和适当的绿色屋顶激励措施的估值。在波特兰州立大学校园的绿色屋顶站点收集的数据将在大学网站上公开访问。结合土壤植物 - 大气连续体的开源照相3模型，将在波特兰州立大学的新生态水文课程中开发易于使用的，动手的模块。合并的数据和模型将在Google合同中将其包装在一起，作为工程社区的教学工具。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。