Estimation of Sensible and Latent Heat Fluxes from Different Ecosystems Using the Daily-Scale Flux Variance Method

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m−2. Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations.

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Publication Details

Journal
Atmosphere
Published
2025-08-30
DOI
https://doi.org/10.3390/atmos16091030
Primary Topic
Plant Water Relations and Carbon Dynamics
Type
article
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article

Estimation of Sensible and Latent Heat Fluxes from Different Ecosystems Using the Daily-Scale Flux Variance Method

Xuhui Lee, Lei Huang, Yini Pu, Wei Xiao et al.
Atmosphere
Plant Water Relations and Carbon Dynamics
article

Estimation of Sensible and Latent Heat Fluxes from Different Ecosystems Using the Daily-Scale Flux Variance Method

Xuhui Lee, Lei Huang, Yini Pu, Wei Xiao, Yanhong Xie, Mi Zhang, Jingzheng Xu
article en

Abstract

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m−2. Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations.

AtmosphereVol. 16(9)
China Meteorological Administration (CN), Nanjing University of Information Science and Technology (CN), Chengdu University of Technology (CN), Yale University (US), Shandong Province Meteorological Bureau (CN), Beijing Municipal Ecological and Environmental Monitoring Center (CN)
Openalex Percentile: Top 78%
Plant Water Relations and Carbon Dynamics
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