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A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto

Author

Listed:
  • Fabio Di Nunno

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Frosinone, Italy)

  • Marco De Matteo

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Frosinone, Italy)

  • Giovanni Izzo

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Frosinone, Italy)

  • Francesco Granata

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Frosinone, Italy)

Abstract

Climate change is having an increasing effect on the water cycle, hindering the proper management of water resources for different purposes. Veneto, Northern Italy, is a region characterized by various climatic conditions, ranging from the coastal area to the inland, which exhibits significant agricultural productivity with high irrigation demand, up to the mountainous area to the north. This study assesses a key aspect of climate change in Veneto by focusing on a crucial hydrological parameter, the reference evapotranspiration (ETo), which is calculated using the Penman–Monteith equation. The K-means algorithm was employed to divide Veneto into nine homogeneous regions, each characterized by specific evapotranspiration and climatic features. Furthermore, the seasonal Mann–Kendall (MK) test and the innovative trends analysis (ITA) method were used to investigate the trends related to monthly precipitation, ETo, and climate variables. The seasonal MK test revealed negative trends in precipitation for all clusters. In contrast, ETo trends appear to be decreasing for some clusters, both on the coast and inland, and increasing for others. The ITA method indicated more pronounced trends for higher values of ETo and precipitation, highlighting significant variations that primarily impact extreme values. Overall, this study’s approach, which incorporates clustering and trends analysis methods, provides a detailed depiction of ETo in Veneto, enabling the identification of distinct homogeneous areas and the assessment of evolutionary trends concerning evapotranspiration and precipitation, from the coastal to the mountainous regions.

Suggested Citation

  • Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:11091-:d:1195160
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    References listed on IDEAS

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    1. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    2. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    3. Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).
    4. Lan Wang & Michael Akritas & Ingrid Van Keilegom, 2008. "An ANOVA-type nonparametric diagnostic test for heteroscedastic regression models," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(5), pages 365-382.
    5. Muhammad S. Ashraf & Ijaz Ahmad & Noor M. Khan & Fan Zhang & Ahmed Bilal & Jiali Guo, 2021. "Streamflow Variations in Monthly, Seasonal, Annual and Extreme Values Using Mann-Kendall, Spearmen’s Rho and Innovative Trend Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(1), pages 243-261, January.
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