@article{10.11648/j.wros.20251405.12,
  author = {Endeshaw Shewangizaw Gebremedhin},
  title = {Assessing ENACTS and CHIRPS Precipitation Estimates in Jimma Zone, Ethiopia
},
  journal = {Journal of Water Resources and Ocean Science},
  volume = {14},
  number = {5},
  pages = {134-146},
  doi = {10.11648/j.wros.20251405.12},
  url = {https://doi.org/10.11648/j.wros.20251405.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.wros.20251405.12},
  abstract = {The availability of satellite and other climate datasets has significantly advanced for hydro-climatic studies. However, these climatic products still face substantial uncertainties. Thus, the main objectives of this paper was to assess the performance of ENACTS and CHIRPS version 2 precipitations from 1991 to 2020 in the daily, monthly, seasonal level, annual and during wet/dry year based on observed ground station data over Jimma Zone, Oromia, Ethiopia. The two products were evaluated by using correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and percentage bias (PBIAS) against gauge data from ground station. On a daily scale, ENACTS exhibits a correlation coefficient (CC) of 0.43, indicating a strong positive relationship with observed rainfall, while CHIRPS has a lower CC of 0.34, reflecting a weaker correlation. ENACTS shows a root mean square error (RMSE) of 8.2 mm and a mean absolute error (MAE) of 4.5 mm, suggesting high accuracy and relatively low average prediction error. In contrast, CHIRPS has an RMSE of 7.4 mm and an MAE of 4.6 mm, indicating fewer discrepancies but slightly less precision. On a monthly scale, ENACTS demonstrates a robust CC of 0.96, significantly outperforming CHIRPS, which has a CC of 0.87. ENACTS's RMSE is 29.9 mm with an MAE of 23.8 mm, while CHIRPS has a higher RMSE of 59.8 mm and an MAE of 47.3 mm. Additionally, ENACTS outperforms CHIRPS across all seasons—Belg, Kiremt, and Bega—with CCs of 0.75, 0.65, and 0.75, respectively, particularly excelling in replicating observed precipitation patterns during the Bega (dry) and Belg (short rainy) seasons. Overall, ENACTS consistently demonstrates superior correlation and accuracy in rainfall predictions compared to CHIRPS across all time scales and seasonal contexts. Therefore, this findings show that Both ENACTS and CHIRPS are more effective at the monthly time scale compared to the daily level, whereas the ENACTS re-mains the more accurate product across all time scales in the Jimma zone. This findings show that both ENACTS and CHIRPS are more effective at the monthly time scale, with correlation coefficients of 0.96 and 0.87, respectively, compared to the daily level, where the coefficients are 0.43 and 0.34. ENACTS remains the more accurate product across all time scales in the Jimma zone. This finding is crucial for guiding the selection of the most suitable precipitation products for agricultural planning, water resource management, and climate adaptation strategies in the region.
},
 year = {2025}
}

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TY  - JOUR
T1  - Assessing ENACTS and CHIRPS Precipitation Estimates in Jimma Zone, Ethiopia

AU  - Endeshaw Shewangizaw Gebremedhin
Y1  - 2025/10/27
PY  - 2025
N1  - https://doi.org/10.11648/j.wros.20251405.12
DO  - 10.11648/j.wros.20251405.12
T2  - Journal of Water Resources and Ocean Science
JF  - Journal of Water Resources and Ocean Science
JO  - Journal of Water Resources and Ocean Science
SP  - 134
EP  - 146
PB  - Science Publishing Group
SN  - 2328-7993
UR  - https://doi.org/10.11648/j.wros.20251405.12
AB  - The availability of satellite and other climate datasets has significantly advanced for hydro-climatic studies. However, these climatic products still face substantial uncertainties. Thus, the main objectives of this paper was to assess the performance of ENACTS and CHIRPS version 2 precipitations from 1991 to 2020 in the daily, monthly, seasonal level, annual and during wet/dry year based on observed ground station data over Jimma Zone, Oromia, Ethiopia. The two products were evaluated by using correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), and percentage bias (PBIAS) against gauge data from ground station. On a daily scale, ENACTS exhibits a correlation coefficient (CC) of 0.43, indicating a strong positive relationship with observed rainfall, while CHIRPS has a lower CC of 0.34, reflecting a weaker correlation. ENACTS shows a root mean square error (RMSE) of 8.2 mm and a mean absolute error (MAE) of 4.5 mm, suggesting high accuracy and relatively low average prediction error. In contrast, CHIRPS has an RMSE of 7.4 mm and an MAE of 4.6 mm, indicating fewer discrepancies but slightly less precision. On a monthly scale, ENACTS demonstrates a robust CC of 0.96, significantly outperforming CHIRPS, which has a CC of 0.87. ENACTS's RMSE is 29.9 mm with an MAE of 23.8 mm, while CHIRPS has a higher RMSE of 59.8 mm and an MAE of 47.3 mm. Additionally, ENACTS outperforms CHIRPS across all seasons—Belg, Kiremt, and Bega—with CCs of 0.75, 0.65, and 0.75, respectively, particularly excelling in replicating observed precipitation patterns during the Bega (dry) and Belg (short rainy) seasons. Overall, ENACTS consistently demonstrates superior correlation and accuracy in rainfall predictions compared to CHIRPS across all time scales and seasonal contexts. Therefore, this findings show that Both ENACTS and CHIRPS are more effective at the monthly time scale compared to the daily level, whereas the ENACTS re-mains the more accurate product across all time scales in the Jimma zone. This findings show that both ENACTS and CHIRPS are more effective at the monthly time scale, with correlation coefficients of 0.96 and 0.87, respectively, compared to the daily level, where the coefficients are 0.43 and 0.34. ENACTS remains the more accurate product across all time scales in the Jimma zone. This finding is crucial for guiding the selection of the most suitable precipitation products for agricultural planning, water resource management, and climate adaptation strategies in the region.

VL  - 14
IS  - 5
ER  - 

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