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<p>Effective management of rain-fed agriculture in drought-prone countries like Ethiopia requires a comprehensive understanding of rainfall variability and improved predictive capabilities. There is a significant research gap remains in understanding the combined interaction and dynamic influence on rainfall patterns in the Upper Blue Nile Basin (UBNb). Moreover, vertically integrated water content and surface temperature have not been adequately explored for their potential to improve rainfall prediction. This study addresses these gaps by investigating the role of the TEJ in shaping summer rainfall variability over the UBNb. We utilize the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2022, specific humidity and wind data from the ECMWF Fifth Generation reanalysis (ERA5) for the period 1979–2022, 200 hPa wind data and SSTs from the NCEP reanalysis (1981–2022) are used both individually and in combination with other climatic variables at Bahir Dar, Debre Markos, Nekemte, and Gore. Ground-based station data from 1990 to 2020 serve to validate two neural network models: NARX-SST and NARX-SST-200 hPa ECMWF. We employed Artificial Neural Network (ANN) models to forecast rainfall by integrating CHIRPS rainfall data, total column water vapor (TCWV), and surface temperature for spatial prediction, while using sea surface temperature (SST), 200 hPa zonal wind, and ECMWF precipitation data for temporal forecasting. The ANN outputs demonstrate a strong association (correlation = 0.99) with the CHIRPS dataset, and low error values are identified. The spatio-temporal analysis significantly enhances the quality and precision of summer rainfall forecasts. This study contributes to improved rainfall prediction critical for sustainable water resource management and agricultural planning in Ethiopia and the broader Nile Basin. The study improves rainfall prediction using ANN models but is limited by the number of stations and variables, suggesting future work should include more data and broader climate drivers for greater accuracy.</p>

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The role of Tropical Easterly Jet on improving the predictability of summer rainfall variability over the Upper Blue Nile Basin in Ethiopia

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Abebe Kebede,
  • Samuel Takele Kenea,
  • Lev D. Labzovskii,
  • Venkatraman Prasanna,
  • B. Preethi

摘要

Effective management of rain-fed agriculture in drought-prone countries like Ethiopia requires a comprehensive understanding of rainfall variability and improved predictive capabilities. There is a significant research gap remains in understanding the combined interaction and dynamic influence on rainfall patterns in the Upper Blue Nile Basin (UBNb). Moreover, vertically integrated water content and surface temperature have not been adequately explored for their potential to improve rainfall prediction. This study addresses these gaps by investigating the role of the TEJ in shaping summer rainfall variability over the UBNb. We utilize the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2022, specific humidity and wind data from the ECMWF Fifth Generation reanalysis (ERA5) for the period 1979–2022, 200 hPa wind data and SSTs from the NCEP reanalysis (1981–2022) are used both individually and in combination with other climatic variables at Bahir Dar, Debre Markos, Nekemte, and Gore. Ground-based station data from 1990 to 2020 serve to validate two neural network models: NARX-SST and NARX-SST-200 hPa ECMWF. We employed Artificial Neural Network (ANN) models to forecast rainfall by integrating CHIRPS rainfall data, total column water vapor (TCWV), and surface temperature for spatial prediction, while using sea surface temperature (SST), 200 hPa zonal wind, and ECMWF precipitation data for temporal forecasting. The ANN outputs demonstrate a strong association (correlation = 0.99) with the CHIRPS dataset, and low error values are identified. The spatio-temporal analysis significantly enhances the quality and precision of summer rainfall forecasts. This study contributes to improved rainfall prediction critical for sustainable water resource management and agricultural planning in Ethiopia and the broader Nile Basin. The study improves rainfall prediction using ANN models but is limited by the number of stations and variables, suggesting future work should include more data and broader climate drivers for greater accuracy.