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<p>In this study, we examined the two main factors of climate change (precipitation and temperature) based on SSP1-2.6, SSP2-4.5, and SSP5-8.5, under three future periods: near future (2015–2043), mid-far future (2044–2072), and far future (2073–2100). Three machine learning models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were compared using statistical metrics: Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and R-squared (R<sup>2</sup>). The ANN outperforms LSTM and SVM in terms of training and testing metrics, particularly NSE and R<sup>2</sup>. The findings showed that temperature and precipitation patterns would increase especially by the end of the century. In the Near Future, SSP5-8.5 estimates a maximum temperature of 43.32&#xa0;°C, SSP1-2.6’s 41.74&#xa0;°C, and SSP2-4.5’s 41.66&#xa0;°C. Minimum temperatures in the Near Future estimate SSP5-8.5 of 47&#xa0;°C, SSP1-2.6 of 37.97&#xa0;°C, and SSP2-4.5 is 36.30&#xa0;°C. Precipitation patterns exhibit notable variations across the different SSPs and future periods. In the Near Future, SSP5-8.5 estimates the highest maximum precipitation at 340.91&#xa0;mm, compared to SSP1-2.6 at 308.34&#xa0;mm and SSP2-4.5 at 329.53&#xa0;mm. This trend of increasing maximum precipitation continues into the mid- and far-future, with SSP5-8.5 maintaining the highest values. Precipitation and temperature projections suggest significant climate changes by the end of the century, with the most extreme scenarios predicting more intense heat and precipitation variability. The increase in precipitation will have positive effects on water availability and hydropower production. On the other hand, higher temperature may increase evaporation and negatively affect water level. Consequently, decision-makers are recommended to take steps to reduce risk and vulnerability, strengthen resilience, enhance well-being, and building the capacity to anticipate and respond successfully to changes posed by climate change in the region of the proposed Pwalugu hydropower plant. The management of the water resources in the region would go a long way to support sustainable agricultural and domestic activities and also enhance energy generation in the region.</p>

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Impact of climate change on the proposed Pwalugu hydropower plant  based on shared socio-economic pathway (SSP) scenarios

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Amos T. Kabo–bah,
  • Emmanuel Kekle Ahialey,
  • Ebenezer Kwadwo Siabi,
  • Samuel Gyamfi

摘要

In this study, we examined the two main factors of climate change (precipitation and temperature) based on SSP1-2.6, SSP2-4.5, and SSP5-8.5, under three future periods: near future (2015–2043), mid-far future (2044–2072), and far future (2073–2100). Three machine learning models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were compared using statistical metrics: Nash–Sutcliffe Efficiency (NSE), Percent Bias (PBias), and R-squared (R2). The ANN outperforms LSTM and SVM in terms of training and testing metrics, particularly NSE and R2. The findings showed that temperature and precipitation patterns would increase especially by the end of the century. In the Near Future, SSP5-8.5 estimates a maximum temperature of 43.32 °C, SSP1-2.6’s 41.74 °C, and SSP2-4.5’s 41.66 °C. Minimum temperatures in the Near Future estimate SSP5-8.5 of 47 °C, SSP1-2.6 of 37.97 °C, and SSP2-4.5 is 36.30 °C. Precipitation patterns exhibit notable variations across the different SSPs and future periods. In the Near Future, SSP5-8.5 estimates the highest maximum precipitation at 340.91 mm, compared to SSP1-2.6 at 308.34 mm and SSP2-4.5 at 329.53 mm. This trend of increasing maximum precipitation continues into the mid- and far-future, with SSP5-8.5 maintaining the highest values. Precipitation and temperature projections suggest significant climate changes by the end of the century, with the most extreme scenarios predicting more intense heat and precipitation variability. The increase in precipitation will have positive effects on water availability and hydropower production. On the other hand, higher temperature may increase evaporation and negatively affect water level. Consequently, decision-makers are recommended to take steps to reduce risk and vulnerability, strengthen resilience, enhance well-being, and building the capacity to anticipate and respond successfully to changes posed by climate change in the region of the proposed Pwalugu hydropower plant. The management of the water resources in the region would go a long way to support sustainable agricultural and domestic activities and also enhance energy generation in the region.