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.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yeşim Ahi, Çiğdem Coşkun Dilcan, Daniyal Durmuş Köksal and Hüseyin Tevfik Gültaş. The first draft of the manuscript was written by Yeşim Ahi and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Highlights
• The study proved that the artificial neural network model is superior in predicting surface evaporation.
• The study reveals the change in current water resource potential based on climate scenarios.
• It is important to make accurate future projections in terms of water resources management.
• The study focused on the irrigated area estimation based on water allocated from reservoir.
• The model could also guide decision-makers and stakeholders in the context of sectoral water needs.
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Ahi, Y., Coşkun Dilcan, Ç., Köksal, D.D. et al. Reservoir Evaporation Forecasting Based on Climate Change Scenarios Using Artificial Neural Network Model. Water Resour Manage 37, 2607–2624 (2023). https://doi.org/10.1007/s11269-022-03365-0
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DOI: https://doi.org/10.1007/s11269-022-03365-0