Simulating the seasonality of a tropical urban park on ENVI-met

This presentation was delivered at the 23rd International Congress of Biometeorology (ICB 2023) on May 14-17, 2023, in Tempe, Arizona, USA.

Rapid urban development and climate change has resulted in urban overheating in many cities across the world. Singapore, a tropical city-state in Southeast Asia, is no exception to this phenomena. Nature-based solutions (NBS) through the provision of publicly accessible urban green parks is increasingly a popular measure to improve urban resilience and mitigation against heat within cities, given its space constraints. The 2030 Singapore Green Plan aims to expand green spaces by up to 1000 hectares as a continual effort to pursue its “City in a Garden'' vision. To assist in the effective execution of such ambitious plans, the relevant agencies have explored various models that can help optimise the deployment of green spaces. ENVI-met (version 5.0.3) is a robust microclimate model that can be used for planning and estimating the cooling benefits from implementing park expansion in dense cities. We evaluate the model performance by simulating Bishan?Ang Mo Kio (BAMK) Park, a medium-sized 62 hectare linear park situated in a densely populated residential area. In other studies, simulations have input boundary conditions for specific days which may not be representative of the seasonality of the area. In this study, our simulation was forced using seven weather types that are representative of the meteorological conditions of Singapore’s tropical hot-humid climate. This allows us to investigate the extent of park cool island for a tropical urban medium-sized park, accounting sufficiently for typical days in the Southwest (SW), Northeast (NE) monsoon and intermonsoon periods. The model is validated using field measurements collected between January 2022 to December 2022. These results show that while ENVI-met is a useful model to project air temperature outputs for urban planning considerations, it is important to input representative boundary conditions and validate the model to account for the seasonality in the respective study areas.