Assessing ENACTS and CHIRPS Precipitation Estimates in Jimma Zone, Ethiopia

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.

Authors

Institutions

Publication Details

Journal
Journal of Water Resources and Ocean Science
Published
2025-10-27
DOI
https://doi.org/10.11648/j.wros.20251405.12
Primary Topic
Precipitation Measurement and Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing ENACTS and CHIRPS Precipitation Estimates in Jimma Zone, Ethiopia

Endeshaw Gebremedhin
Journal of Water Resources and Ocean Science
Precipitation Measurement and Analysis
article

Assessing ENACTS and CHIRPS Precipitation Estimates in Jimma Zone, Ethiopia

Endeshaw Gebremedhin
article en

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.

Journal of Water Resources and Ocean ScienceVol. 14(5)
National Institute of Meteorology (TN)
University of California, Santa Barbara
Openalex Percentile: Top 61%
Precipitation Measurement and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.