Controlling factors of evapotranspiration predictability under diverse climates with the effects of water storage change in the Budyko framework

Chuanhao Wu, Pat J.F. Yeh, Jun Zhou, Jiayun Li, Lulu Zhong, Saisai Wang, Zhengjie Gong, Min Shi, Jiali Ju, Guoru Huang

Research output: Contribution to journalArticleResearchpeer-review

Abstract

The Budyko models (BM) have been extended in previous studies by incorporating water storage change (ΔS) (subtracting ΔS from precipitation) to estimate evapotranspiration (ET) under non-steady state conditions at scales finer than the climatological mean scale. However, a systematic assessment of the interannual ET predictability of the extended BM is still lacking, hence its validity and controlling factors of improvement (over the original BM) under globally diverse climates is not yet well understood. Based on a long-term (1984–2008) gridded water budget data set, we present a comparative analysis of annual ET predictability between the original BM (ET1) and the extended BM considering ΔS (ET2) in 32 global river basins to explore the sensitivity of climate factors and catchment hydrologic responses in determining ET predictability. Results show that the difference between ET1 and ET2 increases linearly with ΔS, with ET2 < ET1 (ET2 > ET1) when ΔS > 0 (ΔS < 0). When both ET1 and ET2 overestimate (underestimate) observed ET, the error in ET2 is smaller than ET1 when ΔS > 0 (ΔS < 0) for all 32 basins considered. When the error signs of ET2 and ET1 differ, however, the difference in the absolute magnitude of ET2 and ET1 errors (REdiff) under extremely humid climates is determined by the difference between potential ET and ET, leading to comparable accuracy between ET2 and ET1. In contrast, under extremely arid climates, REdiff is controlled by the combined influences of ΔS and R, resulting in more accurate ET2 than ET1 under the condition of the in-phase, positive-correlated relationship between ΔS and R.

Original languageEnglish
Article numbere2023WR034499
Number of pages19
JournalWater Resources Research
Volume60
Issue number2
DOIs
Publication statusPublished - Feb 2024

Keywords

  • Budyko framework
  • ET predictability
  • prediction error
  • water storage change

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