Examination of hydrological variations and their effect on water shortage trends and water-energy production using convolutional neural network and ISSA.

Although some moderate wet periods (SPI > + 1.0) are predicted around 2024–2026 and 2034–2036, their intensity and duration are further reduced under SSP5 in a clear trend towards persistent aridification.

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For mid-term conditions, both scenarios project an increasing trend of water scarcity above baseline conditions, with a more pronounced increment of 9.4% in winter scarcity for the SSP5 scenario than the 4.8% increase projected under SSP1, indicating water stress levels possibly 50–100% higher than the baseline mean.

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On the other hand, SSP5 would indicate a slight trend towards increasingly extreme drying conditions, e.g., -2.0 (Jan/Nov/Dec), where >-40% or lower from Near Normal boundary falls within the possible ‘’Severity’’ or ‘’Extreme severity’’ drying categories, possibly having less marked wet periods (e.g., + 0.2, ~+5% from average).

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This close visual and statistical agreement was validated under assessed training data (2004–2018) and testing data (2019–2023) and showed that the model was capable of not only retrieving general seasonal trends, but also accurately resolving extreme hydrological events that are highly significant for operational planning regarding water and power.

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