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Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model

Author

Listed:
  • Yeşim Ahi

    (Ankara University)

  • Çiğdem Coşkun Dilcan

    (Ankara University)

  • Daniyal Durmuş Köksal

    (Ankara University)

  • Hüseyin Tevfik Gültaş

    (Bilecik Seyh Edebali University)

Abstract

Climate plays a dominant role in influencing the process of evaporation and is projected to have adverse effects on water resources especially in the wake of a changing climate. In order to understand the impact of climate change on water resources, artificial intelligence models that possesses rapid decision-making ability, are used. This study was carried out to estimate evaporation in the Karaidemir Reservoir in Turkey with artificial neural networks (ANNs). The daily meteorological data covering the irrigation season were provided for a 30-year reference period and used to develop artificial neural network models. Predicted meteorological data based on climate change projections of HadGEM2-ES and MPI-ESM-MR under the Representative Concentration Pathway (RCP) 4.5 and 8.5 future emissions scenarios between 2000–2098 were utilized for future evaporation projections. The study also focuses on optimal crop patterns and water requirement planning in the future. ANNs model was run for each of the scenarios created based on ReliefF algorithm results using different testing-training-validation rates and learning algorithms of Bayesian Regularization (BR), Levenberg–Marquardt (L-M) and Scaled Conjugate Gradient (SCG). The performance of each alternative model was compared with coefficient of determination (R2) and mean square error (MSE) measures. The obtained results revealed that the ANNs model has high performance in estimation with a few input parameters, statistically. Projected surface water evaporation for the long term (2080–2098) showed an increase of 1.0 and 3.1% for the RCP4.5 scenarios of the MPI and HadGEM model, and a 14% decrease and 7.3% increase for the RCP8.5 scenarios, respectively.

Suggested Citation

  • Yeşim Ahi & Çiğdem Coşkun Dilcan & Daniyal Durmuş Köksal & Hüseyin Tevfik Gültaş, 2023. "Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2607-2624, May.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:6:d:10.1007_s11269-022-03365-0
    DOI: 10.1007/s11269-022-03365-0
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    References listed on IDEAS

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    Cited by:

    1. Jun Liu & Yuyan Zhou & Lihua Chen & Lichuan Wang, 2023. "Assessing the Impact of Climate Change on Water Usage in Typical Industrial Enterprises," Sustainability, MDPI, vol. 15(13), pages 1-18, June.

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