Preprints
https://doi.org/10.5194/hess-2021-126
https://doi.org/10.5194/hess-2021-126

  16 Mar 2021

16 Mar 2021

Review status: this preprint is currently under review for the journal HESS.

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

Daeha Kim1, Minha Choi2, and Jong Ahn Chun3 Daeha Kim et al.
  • 1Department of Civil Engineering, Jeonbuk National University, Jeonju, Jeollabuk-do, 54896, Republic of Korea
  • 2Environment and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon, Gyeonggi-do, 16419, Republic of Korea
  • 3Prediction Research Department, APEC Climate Center, Busan, 48058, Republic of Korea

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.

Daeha Kim et al.

Status: final response (author comments only)

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

Daeha Kim et al.

Daeha Kim et al.

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