Team Members:
Person Name | Person role on project | Affiliation |
---|---|---|
Mutlu Ozdogan | Principal Investigator | University of Wisconsin, Madison, United States |
Urban green spaces are areas of land that are covered with vegetation, including parks, gardens, and other natural areas. These spaces are essential for the health of urban environments, as they provide a range of benefits to both the environment and public health including reducing pollution, regulating temperature, and providing habitats for wildlife, among other benefits. What is less known however, is the influence these green spaces have on environmental energy and water flows that affect urban hydrology and climate. To fill this gap, we propose a multi-city digital twin study of the role of green infrastructure in coupled land-atmosphere prediction. Our proposal has three objectives. First, we plan to document recent and future changes in green infrastructure in select urban areas (Baltimore, Chicago, Houston, and Phoenix as part of the DOE intensive study) using medium- and high resolution satellite observations with the help of deep learning algorithms. Our goal here is to both map highly detailed urban elements such as trees, paved areas, buildings, turf and natural grasses, and to develop biophysical variables such as leaf area index, albedo, emissivity, building heights, and roughness lengths associated with these land use components as inputs to our modeling effort. Second, we will develop a new land model capable of representing green infrastructure at the scale of an entire city. The model will be based on the widely utilized Noah-MP land surface model and integrates previously overlooked surface hydrological processes that are common in urban hydrology. This Noah-MP for Heterogeneous Urban Environments (HUE) includes impervious area-to-vegetation water transfers (e.g., disconnected downspouts) and tree canopy overhanging pavement. HUE also includes more management centric solutions that are common in urban green infrastructure and will allow us to conduct a more realistic hydrologic treatment of urban areas, resolve urban vegetation process like pavement shading and canopy interception, allow for urban energy partitioning that is more representative of real world urban environments, and conduct a realistic coupling between surface and atmospheric conditions within urban regions. HUE is currently coupled with the Weather Research and Forecasting (WRF) model, making it possible to quantify the effects that adaptation policies surrounding urban vegetation and green infrastructure have on the fine-scale urban climate and hydrology for the first time. Finally, we will explore Impacts of urban green spaces on the coupled land-atmosphere system using detailed depictions of land use elements and their biophysical/structural attributes derived from remote sensing and the newly developed Noah-MP HUE coupled with WRF with detailed treatments of urban land use. The proposed work contributes directly to NASA's goal of studying Earth from space to advance scientific understanding and for societal benefits, and NASA's objectives of quantifying global land cover change. It also contributes to NASA's AIST program in developing Earth System Digital Twins (ESDTs) by advancing the incorporation of land-use information in numerical weather forecasting and climate models that contribute in the development of the Land-Earth System Digital Twin (L-ESDT). By providing more realistic representations of urban areas undergoing green transformation, our proposed work will have the potential to offer a digital replica (also known as a digital twin) to monitor and simulate land-atmosphere interactions in urban environments with unprecedented spatio-temporal resolution. We will accomplish this goal by making 1) significant advances in translating Earth observations into land use components; and 2) significant advances in modeling a comprehensive set of processes that are at the heart of improving our understanding of regionalized weather, hydrology, and climate change, as well as advancing forecasting skills.