14 May 2019
14 May 2019
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Uncertainty caused by resistances in evapotranspiration

Wen Li Zhao1,2, Yu Jiu Xiong3,4, Kyaw Tha Paw U4, Pierre Gentine2, Baoyu Chen5, and Guo Yu Qiu1 Wen Li Zhao et al.
  • 1School of Environment and Energy, Peking University Shenzhen Graduate School, Peking University, Shenzhen 518055, China
  • 2Department of Earth and Environmental Engineering, Columbia University, New York, New York, 10027, USA
  • 3School of Civil Engineering, Sun Yat-Sen University, Guangzhou 510275, Guangdong, China
  • 4Department of Land, Air and Water Resources, University of California at Davis, Davis 95618, USA
  • 5Research Institute of Agricultural Resources and Environment, Jilin Academy of Agricultural Sciences, Key Laboratory of Plant Nutrition and Agro-Environment in Northeast Region, Ministry of Agriculture, Changchun, Jilin, China

Abstract. Quantifying the uncertainties induced by resistance parameterization is fundamental to understanding, improving, and developing terrestrial evapotranspiration (ET) models. Using high-density eddy covariance (EC) tower observations in a heterogeneous oasis in Northwest China, this study evaluates the impact of resistances on latent heat flux (LE) estimations, the energy equivalent of ET, by comparing resistance parameterizations with varied complexity under one- and two-source Penman-Monteith (PM) equations. We then discuss possible solutions for reducing such uncertainties by employing a three-temperature (3T) model, which does not explicitly include resistance-related parameters. The results show that the mean absolute percent error (MAPE) varied from 32 % to 39 % for the LE estimates from the one- and two-source PM equations. When only surface resistance (rs) was parameterized under the one-source network, then the uncertainty (defined as the difference between MAPEs) dropped to 12 %. When both rs and aerodynamic resistance (ra) were parameterized differently under the one- and two-source networks, then the uncertainties in the estimates were 11~23 %, emphasizing that multiple resistances add uncertainties. Additionally, the 3T model performed better than the PM equations, with MAPE of 19 %. The results suggest that 1) although prior calibration of the parameters required in resistance estimations can improve the PM-based LE estimates, resistance parameterization process can generate obvious uncertainties, 2) more complex resistance parameterizations leads to more uncertainty in the LE estimation, and 3) the relatively simple 3T model avoids resistance parameterization, thus introducing less uncertainty in the LE estimation.

Wen Li Zhao et al.

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Wen Li Zhao et al.

Wen Li Zhao et al.


Total article views: 2,120 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,753 338 29 2,120 31 37
  • HTML: 1,753
  • PDF: 338
  • XML: 29
  • Total: 2,120
  • BibTeX: 31
  • EndNote: 37
Views and downloads (calculated since 14 May 2019)
Cumulative views and downloads (calculated since 14 May 2019)

Viewed (geographical distribution)

Total article views: 1,697 (including HTML, PDF, and XML) Thereof 1,657 with geography defined and 40 with unknown origin.
Country # Views %
  • 1


Latest update: 02 Feb 2023
Short summary
Accurate evapotranspiration (ET) estimation requires an in-depth identification of uncertainty sources. Using high density eddy covariance observations, we evaluated the effects of resistances on ET estimation and discussed possible solutions. The results show that more complex resistance parameterizations leads to more uncertainty, although prior calibration can improve the ET estimates and that a new model without resistance parameterization introduces less uncertainty into the ET estimation.