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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-260
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-260
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Jul 2020

13 Jul 2020

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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.

A Hybridized NGBoost-XGBoost Framework for Robust Evaporation and Evapotranspiration Prediction

Hakan Başağaoğlu1,, Debaditya Chakraborty2,, and James Winterle1 Hakan Başağaoğlu et al.
  • 1Edwards Aquifer Authority, San Antonio, TX 78215, USA
  • 2University of Texas at San Antonio, San Antonio, TX 78207, USA
  • These authors contributed equally to this work.

Abstract. We analyze the relationship between potential evapotranspiration (ETo), actual evapotranspiration (ETa), and surface water evaporation (Esw) in the semi-arid south-central Texas, using hourly climate data, daily lake evaporation measurements, and daily actual evapotranspiration measurements from an eddy covariance (EC) tower. The deterministic analysis reveals that ETo set the upper bound for ETa, but the lower bound for Esw in the study area. Unprecedentedly, we demonstrate that a newly developed probabilistic machine learning (ML) model, using a hybridized NGBoost-XGBoost framework, can accurately predict the daily ETo, Esw, & ETa from local climate data. The probabilistic approach exhibits great potential in overcoming data uncertainties, in which 99 % of the ETo, 90 % of the Esw, and 91 % of the ETa test data at three watersheds were within the model's 95 % prediction interval. The probabilistic ML model results suggest that the proposed framework can serve as a robust and computationally more efficient tool than the hourly Penman-Monteith equation to predict the ETo while avoiding computationally-involved net solar radiation calculations. Additionally, the performance analysis of the probabilistic ML model indicates that it can be successfully implemented in practice to overcome the uncertainties associated with pan evaporation & pan coefficients in Esw estimates, and to offset the high capital & operational costs of EC towers used for Ea measurements. Finally, we demonstrate, for the first time, a coalition game theory approach to identify the order of importance, dependencies & interactions of climatic variables on the ML-based ETo, Esw, and ETa predictions. New knowledge gained through the game theory approach is beneficial to strategically locate weather stations for enhanced evapo(transpi)ration predictions, and plan out sustainability and resilience efforts, as part of water management and habitat conservation plans.

Hakan Başağaoğlu et al.

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Hakan Başağaoğlu et al.

Hakan Başağaoğlu et al.

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Short summary
We present a new machine learning model to predict evapotranspiration from soil and vegetation cover, and evaporation from a lake using local climate data. We demonstrate that the new model provides accurate predictions without involving complex calculations or expensive data collection methods. Such accurate evapotranspiration predictions are useful for development of sustainable and resilient water management and habitat conservation plans, especially in arid and semi-arid regions.
We present a new machine learning model to predict evapotranspiration from soil and vegetation...
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