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

This research has explored the impacts of insufficient water that is attributed to climate change, as well as industrial growth in power generation. This makes use of Global Circulation Models (GCMs) to project future climate changes and employs the Standardized Precipitation Index (SPI) to determine drought severity. ISSA and CNN work together with him to model and predict changes in reservoir capacity over time. Thus, sustainable hydropower planning in a changing climate shall be achieved. It predicts that from 2025 to 2035, hydrological variability will surely increase since droughts will increase in occurrence and severity compared to the baseline period of 2003 to 2023. It is also expected that SPI values will reach <-1.5, which would indicate the emergence of reduced precipitation volumes of up to 40% below that of the baseline level, as a definitive trend towards drought in the near- to mid-term drought projections (2025-2035). More SPI values above + 1.0, which correlate with precipitation anomalies over 20% above the baseline average, indicate early near-term (2024-2026) and mid-term (2034-2036) wetter conditions. The SSP5 future scenario indicates a relatively drier future, with less water precipitation, higher evaporation rates, and elongated periods of lower reservoir depths. This optimized CNN model provides an accurate, reliable reservoir dynamics simulation (R2 = 0.89, NSE = 0.80) that is essential for a precise hydropower forecasting. Hydropower generation peaks around 2026 under SSP1, declining by about 0.7 billion kWh by 2030 and slightly stabilizing above that level by 2040. Such results indicate how pertinent it is for adaptive water-energy management, bearing in mind the increasing hydrological variability induced by climate change.