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<p>The accurate characterization of moisture availability is critical for effective drought monitoring, particularly in data-scarce tropical regions like West Africa, where climate change exacerbates agricultural and water security challenges. This study addresses a critical research gap by evaluating the sensitivity of three moisture indices—Standardized Precipitation-Evapotranspiration Index (SPEI), Thornthwaite Moisture Index (MI), and Budyko Aridity Index (AI)—to six potential evapotranspiration (PET) models (Penman-Monteith, Priestley-Taylor, Hargreaves, Blaney-Morin-Nigeria, Jensen-Haise, and Thornthwaite) across Nigeria’s diverse climatic zones. Utilizing high-quality meteorological data from three International Institute of Tropical Agriculture stations (Onne, Ibadan, and Kano), the study employs six robust evaluation metrics to assess model performance. Findings reveal significant statistical differences in PET estimates compared to the Penman-Monteith standard, with Hargreaves and Blaney-Morin-Nigeria models demonstrating high correlation (<i>r</i> ≥ 0.79) and low RMSE (&lt; 40.4&#xa0;mm) across stations, making them viable alternatives in data-limited settings. Notably, AI and MI exhibit greater sensitivity to PET model choice than SPEI, with AI varying by up to 67% and MI by 33–48% across models, highlighting the critical need for careful PET model selection. These results provide actionable guidance for enhancing drought monitoring frameworks in Nigeria and offer a methodological template for similar evaluations in other tropical regions facing climate change and data constraints.</p> Graphical Abstract <p>The graphical abstract visually synthesizes the study’s comprehensive evaluation of moisture index sensitivity to potential evapotranspiration (PET) models across Nigeria’s diverse tropical climate zones. It illustrates the comparative analysis of six distinct PET models (Penman-Monteith, Priestley-Taylor, Hargreaves, Blaney-Mornin-Nigeria, Jensen-Haise, and Thornthwaite) and their influence on three critical moisture indices (SPEI, MI, and AI). The graphic depicts the geographical distribution of the three IITA stations (Onne, Ibadan, and Kano) representing humid, sub-humid, and semi-arid climatic zones, respectively, with corresponding data visualization of model performance metrics. Central to the abstract is the demonstration of how different PET formulations affect drought characterization across these diverse environments, highlighting that aridity (AI) and moisture indices (MI) exhibit greater sensitivity to PET model selection than SPEI. The graphic visually reinforces the study’s key finding that Hargreaves (HG) and Blaney-Morin-Nigeria (BMN) emerge as robust alternatives to the data-intensive Penman-Monteith model in data-scarce tropical environments. This visual representation is particularly significant as it encapsulates the first comprehensive West African assessment of how PET model selection impacts multiple moisture indices simultaneously, providing crucial guidance for drought monitoring in regions facing similar data constraints and climate change challenges.</p>

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Evaluating the Sensitivity of Moisture Indices to Six PET Models in a Comparative Study

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Philip G. Oguntunde,
  • Gunnar Lischeid,
  • Akinwumiju A. Shola,
  • Akinwale T. Ogunrinde

摘要

The accurate characterization of moisture availability is critical for effective drought monitoring, particularly in data-scarce tropical regions like West Africa, where climate change exacerbates agricultural and water security challenges. This study addresses a critical research gap by evaluating the sensitivity of three moisture indices—Standardized Precipitation-Evapotranspiration Index (SPEI), Thornthwaite Moisture Index (MI), and Budyko Aridity Index (AI)—to six potential evapotranspiration (PET) models (Penman-Monteith, Priestley-Taylor, Hargreaves, Blaney-Morin-Nigeria, Jensen-Haise, and Thornthwaite) across Nigeria’s diverse climatic zones. Utilizing high-quality meteorological data from three International Institute of Tropical Agriculture stations (Onne, Ibadan, and Kano), the study employs six robust evaluation metrics to assess model performance. Findings reveal significant statistical differences in PET estimates compared to the Penman-Monteith standard, with Hargreaves and Blaney-Morin-Nigeria models demonstrating high correlation (r ≥ 0.79) and low RMSE (< 40.4 mm) across stations, making them viable alternatives in data-limited settings. Notably, AI and MI exhibit greater sensitivity to PET model choice than SPEI, with AI varying by up to 67% and MI by 33–48% across models, highlighting the critical need for careful PET model selection. These results provide actionable guidance for enhancing drought monitoring frameworks in Nigeria and offer a methodological template for similar evaluations in other tropical regions facing climate change and data constraints.

Graphical Abstract

The graphical abstract visually synthesizes the study’s comprehensive evaluation of moisture index sensitivity to potential evapotranspiration (PET) models across Nigeria’s diverse tropical climate zones. It illustrates the comparative analysis of six distinct PET models (Penman-Monteith, Priestley-Taylor, Hargreaves, Blaney-Mornin-Nigeria, Jensen-Haise, and Thornthwaite) and their influence on three critical moisture indices (SPEI, MI, and AI). The graphic depicts the geographical distribution of the three IITA stations (Onne, Ibadan, and Kano) representing humid, sub-humid, and semi-arid climatic zones, respectively, with corresponding data visualization of model performance metrics. Central to the abstract is the demonstration of how different PET formulations affect drought characterization across these diverse environments, highlighting that aridity (AI) and moisture indices (MI) exhibit greater sensitivity to PET model selection than SPEI. The graphic visually reinforces the study’s key finding that Hargreaves (HG) and Blaney-Morin-Nigeria (BMN) emerge as robust alternatives to the data-intensive Penman-Monteith model in data-scarce tropical environments. This visual representation is particularly significant as it encapsulates the first comprehensive West African assessment of how PET model selection impacts multiple moisture indices simultaneously, providing crucial guidance for drought monitoring in regions facing similar data constraints and climate change challenges.