Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset
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Published:2022-04-08
Issue:7
Volume:15
Page:3041-3078
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ISSN:1991-9603
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Container-title:Geoscientific Model Development
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language:en
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Short-container-title:Geosci. Model Dev.
Author:
Omidvar HamidrezaORCID, Sun TingORCID, Grimmond SueORCID, Bilesbach Dave, Black AndrewORCID, Chen JiquanORCID, Duan Zexia, Gao Zhiqiu, Iwata Hiroki, McFadden Joseph P.
Abstract
Abstract. To compare the impact of surface–atmosphere exchanges from rural and urban areas, fully vegetated areas (e.g. deciduous trees, evergreen trees and
grass) commonly found adjacent to cities need to be modelled. Here we provide a general workflow to derive parameters for SUEWS (Surface Urban
Energy and Water Balance Scheme), including those associated with vegetation phenology (via leaf area index, LAI), heat storage and surface
conductance. As expected, attribution analysis of bias in SUEWS-modelled QE finds that surface conductance (gs) plays the
dominant role; hence there is a need for more estimates of surface conductance parameters. The workflow is applied at 38 FLUXNET sites. The derived
parameters vary between sites with the same plant functional type (PFT), demonstrating the challenge of using a single set of parameters for a
PFT. SUEWS skill at simulating monthly and hourly latent heat flux (QE) is examined using the site-specific derived parameters, with the
default NOAH surface conductance parameters (Chen et al., 1996). Overall evaluation for 2 years has similar metrics for both configurations:
median hit rate between 0.6 and 0.7, median mean absolute error less than 25 W m−2, and median mean bias error
∼ 5 W m−2. Performance differences are more evident at monthly and hourly scales, with larger mean bias error (monthly:
∼ 40 W m−2; hourly ∼ 30 W m−2) results using the NOAH-surface conductance parameters, suggesting that they
should be used with caution. Assessment of sites with contrasting QE performance demonstrates how critical capturing the LAI dynamics is
to the SUEWS prediction skills of gs and QE. Generally gs is poorest in cooler periods (more pronounced at
night, when underestimated by ∼ 3 mm s−1). Given the global LAI data availability and the workflow provided in this study, any
site to be simulated should benefit.
Funder
Natural Environment Research Council Met Office National Natural Science Foundation of China
Publisher
Copernicus GmbH
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