Water Resources Research

Modeling interannual variability of seasonal evaporation and storage change based on the extended Budyko framework

Authors

  • Xi Chen,

    1. Department of Civil Environmental and Construction Engineering, University of Central Florida, Orlando, Florida, USA
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  • Negin Alimohammadi,

    1. Department of Civil Environmental and Construction Engineering, University of Central Florida, Orlando, Florida, USA
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  • Dingbao Wang

    Corresponding author
    1. Department of Civil Environmental and Construction Engineering, University of Central Florida, Orlando, Florida, USA
    • Corresponding author: D. Wang, Department of Civil, Environmental, and Construction Engineering, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816, USA (dingbao.wang@ucf.edu)

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Abstract

[1] Long-term climate is the first-order control on mean annual water balance, and vegetation and the interactions between climate seasonality and soil water storage change have also been found to play important roles. The purpose of this paper is to extend the Budyko hypothesis to the seasonal scale and to develop a model for interannual variability of seasonal evaporation and storage change. A seasonal aridity index is defined as the ratio of potential evaporation to effective precipitation, where effective precipitation is the difference between rainfall and storage change. Correspondingly, evaporation ratio is defined as the ratio of evaporation to effective precipitation. A modified Turc-Pike equation with a horizontal shift is proposed to model interannual variability of seasonal evaporation ratio as a function of seasonal aridity index, which includes rainfall seasonality and soil water change. The performance of the seasonal water balance model is evaluated for 277 watersheds in the United States. The 99% of wet seasons and 90% of dry seasons have Nash-Sutcliffe efficiency coefficients larger than 0.5. The developed seasonal model can be applied for constructing long-term evaporation and storage change data when rainfall, potential evaporation, and runoff observations are available. On the other hand, vegetation affects seasonal water balance by controlling both evaporation and soil moisture dynamics. The correlation between NDVI and evaporation is strong particularly in wet seasons. However, the correlation between NDVI and the seasonal model parameters is only strong in dry seasons.

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