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<p>Reliable estimation of reference evapotranspiration (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(ET_0\)</EquationSource> </InlineEquation>) is essential for water resources management and agricultural planning. However, the application of the standard FAO Penman–Monteith method (PM-FAO56) is often constrained by the limited availability of complete meteorological datasets. This study assesses the performance of 15 alternative <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(ET_0\)</EquationSource> </InlineEquation> estimation methods across 12 locations in the state of Mato Grosso, Brazil, encompassing the Amazon, Cerrado (Tropical Savanna), and Amazon–Cerrado Transition biomes. An 11-year historical series (2014–2024) of hourly and daily meteorological data from INMET stations was used as reference. Model performance was evaluated using correlation (<i>r</i>), agreement index (<i>d</i>), confidence coefficient (<i>c</i>), and root mean square error (RMSE). Results reveal clear biome-dependent differences in model performance. In the Amazon biome, where evapotranspiration is predominantly constrained by the available radiative energy component of the PM-FAO56 equation, radiation-based methods exhibited superior accuracy, with the Turc (TC) method consistently achieving the highest performance. In the Cerrado, marked climatic seasonality led to alternating dominance of radiative and aerodynamic processes, favoring methods that combine radiation and temperature, particularly the Radiation–Temperature (RT) and Turc methods. In contrast, the Amazon–Cerrado Transition biome presented the greatest modeling challenges due to environmental heterogeneity and land-use dynamics; nevertheless, TC and RT remained the most reliable options when locally calibrated. These findings highlight the importance of biome-specific evaluation and local calibration to ensure reliable evapotranspiration estimates for practical hydrological and agricultural applications.</p>

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Assessment of evapotranspiration estimation models in brazilian tropical savanna and amazonian biomes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月10日

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  • Gustavo Costa Queiroz,
  • Bruno Martins Mendes Vieira,
  • Junior Gonçalves da Silva,
  • Leone Francisco Amorim Curado,
  • Marcelo Sacardi Biudes,
  • Daniela de Oliveira Maionchi

摘要

Reliable estimation of reference evapotranspiration ( \(ET_0\) ) is essential for water resources management and agricultural planning. However, the application of the standard FAO Penman–Monteith method (PM-FAO56) is often constrained by the limited availability of complete meteorological datasets. This study assesses the performance of 15 alternative \(ET_0\) estimation methods across 12 locations in the state of Mato Grosso, Brazil, encompassing the Amazon, Cerrado (Tropical Savanna), and Amazon–Cerrado Transition biomes. An 11-year historical series (2014–2024) of hourly and daily meteorological data from INMET stations was used as reference. Model performance was evaluated using correlation (r), agreement index (d), confidence coefficient (c), and root mean square error (RMSE). Results reveal clear biome-dependent differences in model performance. In the Amazon biome, where evapotranspiration is predominantly constrained by the available radiative energy component of the PM-FAO56 equation, radiation-based methods exhibited superior accuracy, with the Turc (TC) method consistently achieving the highest performance. In the Cerrado, marked climatic seasonality led to alternating dominance of radiative and aerodynamic processes, favoring methods that combine radiation and temperature, particularly the Radiation–Temperature (RT) and Turc methods. In contrast, the Amazon–Cerrado Transition biome presented the greatest modeling challenges due to environmental heterogeneity and land-use dynamics; nevertheless, TC and RT remained the most reliable options when locally calibrated. These findings highlight the importance of biome-specific evaluation and local calibration to ensure reliable evapotranspiration estimates for practical hydrological and agricultural applications.