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Application of non-conventional soft computing approaches for estimation of reference evapotranspiration in various climatic regions

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Abstract

Evapotranspiration (ET) is a fundamental element in the hydrological cycle and plays a vital role in simulating hydrological effects of climate change. Accurate estimation of reference evapotranspiration (ETo) plays a significant role in the field of hydrometeorology and agrometeorology. There are many direct or indirect methods that are employed for estimation of ETo. Furthermore, soft computing approaches are also used to solve complex problems of estimation in various disciplines. The application of the soft computing techniques for estimating evapotranspiration has only been limited to few approaches including artificial neural network, fuzzy logic, and genetic algorithm. This study is conducted to apply non-conventional soft computing approaches for estimating ETo which include single decision tree (SDT), tree boost (TB), and decision tree forest (DTF). Monthly meteorological data spanning over 30 years (1987 to 2016) for six different cities located in arid, semi-arid, and humid regions of Pakistan is used for the estimation of ETo in this study. Seventeen input combinations comprising of various climatic parameters were developed to evaluate the impact of different parameters. A total of 306 models were developed using the SDT, TB, and DTF approaches for all six selected cities of Pakistan. The results of the developed models are then compared with ETo calculated by the standard method of modified Penmen equation. The TB modeling technique is found to perform best for various cities located in different climatic zones of Pakistan. Only two climatic parameters, the mean temperature and wind velocity, are found to be sufficient for efficient ETo prediction. To further validate the results, the outcome of the study was applied on some other climatic regions located in the USA, New Zealand, and China. Finally, it is concluded that these non-conventional approaches can also be considered superior alternatives to the available methods of estimating ETo.

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Acknowledgments

Authors pay gratitude to the Pakistan Meteorological Department (PMD) for the provision of recorded data. We also acknowledge the anonymous reviewers whose constructive suggestions help us to improve this manuscript.

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Authors and Affiliations

  1. Department of Agricultural Engineering, Bahauddin Zakariya University, Multan, Pakistan

    Ali Raza, Muhammad Shoaib, Afzaal Khan & Faisal Baig

  2. College of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, China

    Muhammad Abrar Faiz

  3. Department of Civil Engineering, Bahauddin Zakariya University, Multan, Pakistan

    Mudasser Muneer Khan

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  1. Ali Raza
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  2. Muhammad Shoaib
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  3. Afzaal Khan
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  4. Faisal Baig
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  5. Muhammad Abrar Faiz
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  6. Mudasser Muneer Khan
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Correspondence to Muhammad Shoaib.

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Highlights

• Reference evapotranspiration is estimated using climatic data in various climatic regions.

• Soft computing approaches including decision tree, tree boost, and decision tree forest are used for estimating ETo.

• Tree boost is found to produce accurate results for all the climatic regions.

• Mean temperature and wind speed are found to be more significant parameters.

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Raza, A., Shoaib, M., Khan, A. et al. Application of non-conventional soft computing approaches for estimation of reference evapotranspiration in various climatic regions. Theor Appl Climatol 139, 1459–1477 (2020). https://doi.org/10.1007/s00704-019-03007-3

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  • DOI: https://doi.org/10.1007/s00704-019-03007-3

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