Evapotranspiration estimation in heterogeneous urban vegetation
Abstract:Finding a valid approach to measure the water requirements of mixed urban vegetation is a challenge. Evapotranspiration (ET) is the main component of a plant’s water requirement. A better understanding of the ET of urban vegetation is essential for sustainable urbanisation. Increased implementation of green infrastructure will be informed by this work. Despite promising technologies and sophisticated facilities, ET estimation of urban vegetation remains insufficiently characterized.
We reviewed the common field, laboratory and modelling techniques for ET estimation, mostly agriculture and forestry applications. We opted for 3 approaches of ET estimation: 1) an observational-based method using adjustment factors applied to reference ET, 2) a field-based method of Soil Water Balance (SWB) and 3) a Remote Sensing (RS)-based method. These approaches were applied to an experimental site to evaluate the most suitable ET estimation approach for an urban parkland.
To determine in-situ ET, 2 lysimeters and 4 Neutron Moisture Meter probes were installed. Based on SWB principles, all input water (irrigation, precipitation and upward groundwater movements) and output water (ET, drainage, soil moisture and runoff) were measured monthly for 14 months. The observation based approach and the ground-based approach (SWB) were compared. Our predictions were compared to the actual irrigation rates (data provided by the City Council). Results suggest the observational-based method is the most appropriate urban ET estimation.
We examined the capability of RS to estimate ET for urban vegetation. Image processing of 5 WorldView2 satellite images enabled modelling of the relationship between urban vegetation and vegetation indices derived from high resolution images. Our results indicate that an ETobservational-based -NDVI modelling approach is a reliable method of ET estimation for mixed urban vegetation. It also has the advantage of not depending on extensive field data collection.