Comparing Three Approaches of Evapotranspiration Estimation in Mixed Urban Vegetation: Field-Based, Remote Sensing-Based and Observational-Based Methods

Abstract

Despite being the driest inhabited continent, Australia has one of the highest per capita water consumptions in the world. In addition, instead of having fit-for-purpose water supplies (using different qualities of water for different applications), highly treated drinking water is used for nearly all of Australia's urban water supply needs, including landscape irrigation. The water requirement of urban landscapes, particularly urban parklands, is of growing concern. The estimation of evapotranspiration (ET) and subsequently plant water requirements in urban vegetation needs to consider the heterogeneity of plants, soils, water, and climate characteristics. This research contributes to a broader effort to establish sustainable irrigation practices within the Adelaide Parklands in Adelaide, South Australia. In this paper, two practical ET estimation approaches are compared to a detailed Soil Water Balance (SWB) analysis over a one year period. One approach is the Water Use Classification of Landscape Plants (WUCOLS) method, which is based on expert opinion on the water needs of different classes of landscape plants. The other is a remote sensing approach based on the Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra satellite. Both methods require knowledge of reference ET calculated from meteorological data. The SWB determined that plants consumed 1084 mm¨yr´1 of water in ET with an additional 16% lost to drainage past the root zone, an amount sufficient to keep salts from accumulating in the root zone. ET by MODIS EVI was 1088 mm¨yr´1, very close to the SWB estimate, while WUCOLS estimated the total water requirement at only 802 mm¨yr´1, 26% lower than the SWB estimate and 37% lower than the amount actually added including the drainage fraction. Individual monthly ET by MODIS was not accurate, but these errors were cancelled out to give good agreement on an annual time step. We conclude that the MODIS EVI method can provide accurate estimates of urban water requirements in mixed landscapes large enough to be sampled by MODIS imagery with 250-m resolution such as parklands and golf courses.

Evapotranspiration of urban landscapes in Los Angeles, California at the municipal scale

“…Yearly ET veg was also similar to ET of dense meadows in the Owens Valley of California at the base of the Sierra Nevada Mountain range, which have the highest ET among native landscapes in California [ Duell , ; Steinwand et al ., ]. In addition, yearly ET veg was similar to an irrigated park in a dry Mediterranean region of Adelaide, Australia [ Nouri et al ., ]. It was also within the range of transpiration from tropical tree plantations (1000–1500 mm d −1 ) [ Larcher , ].…”

Comparison of evapotranspiration estimates based on the surface water balance, modified Penman‐Monteith model, and reanalysis data sets for continental China

“…Nouri et al . [] compared three ET estimation approaches—field‐, remote‐sensing‐, and observation‐based methods—for mixed urban vegetation and concluded that the remote sensing method could generate accurate ET estimates. However, because of the different model structures and input data sets involved, comparisons have been difficult to assess and have led to large differences between ET estimates.…”

Land–Atmosphere Exchange of Water and Heat in the Arid Mountainous Grasslands of Central Asia during the Growing Season

“…Remote sensing and modeling methods allow for the characteristics of the land-atmosphere exchange of water and heat to be studied over large regions, but the results are often highly uncertain due to defects in computational methods and difficulty in determining initial parameter values [22,23]. In particular, there is a serious shortage of observational water and heat data for the arid mountainous grassland ecosystems in Central Asia, making it impossible to fully verify the findings obtained from remote sensing and modeling.…”