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<p>The investigation employs precipitation and temperature data from CORDEX-Africa and Prediction of Worldwide Energy Resources (POWER) datasets, analyzing them under Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. The datasets underwent bias correction using the climate model data hydrologic modeling tool (CMhyd) and a 24-year climate forecast system reanalysis data. The bias-corrected ensemble precipitation demonstrated excellent performance, with <i>R</i><sup>2</sup>, Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS) values at 99.4%, 99.1%, 10.1&#xa0;mm, and 3.4%, respectively. Likewise, the bias-corrected maximum and minimum temperatures showed <i>R</i><sup>2</sup>, NSE, RMSE, and PBIAS values of 95.3%, 94.9%, 0.165&#xa0;°C, and 0.25%, and 95.7%, 96.2%, 0.23&#xa0;°C, and 0.4%, respectively. Projected precipitation changes show seasonal variation under both RCP scenarios. Rainfall is expected to increase during the wet season and decrease in the dry season, reflecting uncertainties in climate models. The 12-month Standardized Precipitation Index suggests more frequent, prolonged droughts. Rising trends in average monthly maximum temperatures are projected, especially under higher greenhouse gas scenarios. Both RCP4.5 and RCP8.5 indicate greater drought, intense rainfall, and warmer temperatures in the Imo Basin, emphasizing the urgent need for adaptive strategies to conserve water, reduce emissions, and protect ecosystem resources.</p>

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Exploring CORDEX-Africa Climate Simulation and Statistical Bias Correction Technique for Hydrological Impact Assessment in Imo River Basin

  • 期刊论文

  • 发表日期：

    2025年7月28日

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  • Oloche Robert Ekwule,
  • Jonah Chukwuemeka Agunwamba

摘要

The investigation employs precipitation and temperature data from CORDEX-Africa and Prediction of Worldwide Energy Resources (POWER) datasets, analyzing them under Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. The datasets underwent bias correction using the climate model data hydrologic modeling tool (CMhyd) and a 24-year climate forecast system reanalysis data. The bias-corrected ensemble precipitation demonstrated excellent performance, with R2, Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS) values at 99.4%, 99.1%, 10.1 mm, and 3.4%, respectively. Likewise, the bias-corrected maximum and minimum temperatures showed R2, NSE, RMSE, and PBIAS values of 95.3%, 94.9%, 0.165 °C, and 0.25%, and 95.7%, 96.2%, 0.23 °C, and 0.4%, respectively. Projected precipitation changes show seasonal variation under both RCP scenarios. Rainfall is expected to increase during the wet season and decrease in the dry season, reflecting uncertainties in climate models. The 12-month Standardized Precipitation Index suggests more frequent, prolonged droughts. Rising trends in average monthly maximum temperatures are projected, especially under higher greenhouse gas scenarios. Both RCP4.5 and RCP8.5 indicate greater drought, intense rainfall, and warmer temperatures in the Imo Basin, emphasizing the urgent need for adaptive strategies to conserve water, reduce emissions, and protect ecosystem resources.