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Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios

Author

Listed:
  • Junaid Maqsood

    (University of Prince Edward Island)

  • Aitazaz A. Farooque

    (University of Prince Edward Island
    University of Prince Edward Island)

  • Farhat Abbas

    (University of Prince Edward Island)

  • Travis Esau

    (Dalhousie University)

  • Xander Wang

    (University of Prince Edward Island)

  • Bishnu Acharya

    (University of Saskatchewan)

  • Hassan Afzaal

    (University of Prince Edward Island)

Abstract

Evapotranspiration is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island using their two input parameters: daily maximum temperature (Tmax), and daily minimum temperature (Tmin). The Tmax and Tmin were downscaled with the help of statistical downscaling model (SDSM) for three future periods 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations. For RCP8.5, it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers in future quantification of the availability of water for the island and to optimize the use of island water resources on a sustainable basis.

Suggested Citation

  • Junaid Maqsood & Aitazaz A. Farooque & Farhat Abbas & Travis Esau & Xander Wang & Bishnu Acharya & Hassan Afzaal, 2022. "Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 835-851, February.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:3:d:10.1007_s11269-021-02997-y
    DOI: 10.1007/s11269-021-02997-y
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    References listed on IDEAS

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    1. Muhammad Hafeez & Zia Ahmad Chatha & Alamgir Akhtar Khan & Allah Bakhsh & Abdul Basit & Fatima Tahira, 2020. "Estimating Reference Evapotranspiration by Hargreaves and Blaney-Criddle Methods in Humid Subtropical Conditions," Current Research in Agricultural Sciences, Conscientia Beam, vol. 7(1), pages 15-22.
    2. Zhao, Xiaohu & Huang, Guohe & Lu, Chen & Zhou, Xiong & Li, Yongping, 2020. "Impacts of climate change on photovoltaic energy potential: A case study of China," Applied Energy, Elsevier, vol. 280(C).
    3. Muhammad Hafeez & Zia Ahmad Chatha & Alamgir Akhtar Khan & Allah Bakhsh & Abdul Basit & Fatima Tahira, 2020. "Estimating Reference Evapotranspiration by Hargreaves and Blaney-Criddle Methods in Humid Subtropical Conditions," Current Research in Agricultural Sciences, Conscientia Beam, vol. 7(1), pages 15-22.
    4. Junaid Maqsood & Aitazaz A. Farooque & Xander Wang & Farhat Abbas & Bishnu Acharya & Hassan Afzaal, 2020. "Contribution of Climate Extremes to Variation in Potato Tuber Yield in Prince Edward Island," Sustainability, MDPI, vol. 12(12), pages 1-15, June.
    5. Shuang Zhu & Zhanya Xu & Xiangang Luo & Chao Wang & Hairong Zhang, 2019. "Quantifying the Contributions of Climate Change and Human Activities to Drought Extremes, Using an Improved Evaluation Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5051-5065, December.
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    Cited by:

    1. Long Zhao & Liwen Xing & Yuhang Wang & Ningbo Cui & Hanmi Zhou & Yi Shi & Sudan Chen & Xinbo Zhao & Zhe Li, 2023. "Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1207-1222, February.
    2. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.

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