Preprints
https://doi.org/10.5194/hess-2021-126
https://doi.org/10.5194/hess-2021-126
16 Mar 2021
 | 16 Mar 2021
Status: this preprint was under review for the journal HESS but the revision was not accepted.

A continental-scale evaluation of the calibration-free complementary relationship with physical, machine-learning, and land-surface models

Daeha Kim, Minha Choi, and Jong Ahn Chun

Abstract. The widespread negative correlation between the atmospheric vapor pressure deficit and soil moisture lends strong support to the complementary relationship (CR) of evapotranspiration. While it has showed outstanding performance in predicting actual evapotranspiration (ETa) over land surfaces, the calibration-free CR formulation has not been tested in the Australian continent dominantly under (semi-)arid climates. In this work, we comparatively evaluated its predictive performance with seven physical, machine-learning, and land surface models for the continent at a 0.5° × 0.5° grid resolution. Results showed that the calibration-free CR that forces a single parameter to everywhere produced considerable biases when comparing to water-balance ETa (ETwb). The CR method was unlikely to outperform the other physical, machine-learning, and land surface models, overrating ETa in (semi-)humid coastal areas for 2002–2012 while underestimating in arid inland locations. By calibrating the parameter against water-balance ETa independent of the simulation period, the CR method became able to outperform the other models in reproducing the spatial variation of the mean annual ETwb and the interannual variation of the continental means of ETwb. However, interannual the grid-scale variability and trends were captured unacceptably even after the calibration. The calibrated parameters for the CR method were significantly correlated with the mean net radiation, temperature, and wind speed, implying that (multi-)decadal climatic variability could diversify the optimal parameters for the CR method. The other physical, machine-learning, and land surface models provided a consistent indication with the prior global-scale assessments. We also argued that at least some surface information is necessary for the CR method to describe long-term hydrologic cycles at the grid scale.

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Daeha Kim, Minha Choi, and Jong Ahn Chun

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-126', Anonymous Referee #1, 17 Mar 2021
    • AC1: 'Reply on RC1', Jong Ahn Chun, 08 Jun 2021
  • RC2: 'Comment on hess-2021-126', Joshua Fisher, 25 Mar 2021
    • AC2: 'Reply on RC2', Jong Ahn Chun, 08 Jun 2021
  • AC3: 'correction of the authors' response to the referee 1's comment on Eq. (7)', Jong Ahn Chun, 25 Jan 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-126', Anonymous Referee #1, 17 Mar 2021
    • AC1: 'Reply on RC1', Jong Ahn Chun, 08 Jun 2021
  • RC2: 'Comment on hess-2021-126', Joshua Fisher, 25 Mar 2021
    • AC2: 'Reply on RC2', Jong Ahn Chun, 08 Jun 2021
  • AC3: 'correction of the authors' response to the referee 1's comment on Eq. (7)', Jong Ahn Chun, 25 Jan 2022
Daeha Kim, Minha Choi, and Jong Ahn Chun
Daeha Kim, Minha Choi, and Jong Ahn Chun

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Short summary
This work evaluate a convenient operational method to simulate evaporation over dry land surfaces across Australia. While this chosen method based on the responsive behavior of atmospheric water demand outperformed commonly-used sophisticated models in predicting evaporation in the United States and China, it showed some poor performance in wet river basins Australia. Yet, its performance was still good under (semi-)arid climates.