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Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset

Omidvar, H., Sun, T. ORCID:, Grimmond, S. ORCID:, Bilesbach, D., Black, A., Jiquan, C., Zexia, D., Zhiqiu, G., Iwata, H. and McFadden, J. P. (2022) Surface Urban Energy and Water Balance Scheme (v2020a) in vegetated areas: parameter derivation and performance evaluation using FLUXNET2015 dataset. Geoscientific Model Development, 15 (7). pp. 3041-3078. ISSN 1991-9603

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To link to this item DOI: 10.5194/gmd-15-3041-2022


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.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:104567
Publisher:Copernicus for European Geosciences Union


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