DATA QUALITY ASSESSMENT AND HOMOGENIZATION OF RAINFALL TIME SERIES IN DATA-SCARCE REGIONS: A CASE STUDY OF THE UPPER OUM ER-RBIA BASIN, NORTHERN MOROCCO
Malika EL-HAMDOUNY1,*, Manal EL GAROUANI1, Maryam EL-YAZIDI1, Abdelbaset MIDAOUI2, Mimoun GOUMIH3 & Abderrahim LAHRACH1
1Functional Ecology and Environmental Engineering Laboratory, Faculty of Science and Technology of Fez, Sidi Mohammed Benabdellah University, Morocco
2Data Science for Sustainable Earth Laboratory (Data4Earth), Faculty of Science and Technology of Beni Mellal, Sultan Moulay Slimane University, Morocco
3Dynamic of Landscapes, Risks and Heritage Laboratory, Faculty of Letters and Human Sciences of Beni Mellal, Sultan Moulay Slimane University, Morocco
* Corresponding author: malika.elhamdouny@usmba.ac.ma
Download PDF document
Downloads: 778
0 citation(s) in Scopus
Abstract
DOI: 10.26471/cjees/2025/020/317
Data quality is crucial for the reliability and accuracy of hydroclimatic studies. In data-scarce regions, time series are often incomplete, with many gaps and outliers. Moreover, changes in station location, instrument, or other conditions can cause shifts unrelated to natural variability, leading to false conclusions about climate trends. This study evaluates the quality and homogeneity of rainfall time series to fill in missing values and identify non-climatic inconsistencies. It focuses on the Upper Oum Er-Rbia basin in northern Morocco. To ensure the integrity of monthly rainfall data in six-gauge stations from 1970 to 2022, the Standard Normal Homogeneity Test (SNHT) method is employed in the R environment. As a result, quality control detected 12 outliers. The homogeneity test highlighted four breaks. The one observed in station S1 can be related to the construction of the Ahmed El-Hansali dam. The homogenized series consists of 79.3% of observed data, 6.3% of filled-in data, and 14.4% of corrected data. The RMSE calculated on the anomaly series shows low values with an average of 15.4 mm, indicating good performance of the SNHT test. The findings highlight the significance of employing rigorous statistical methods like SNHT to detect anomalies and ensure the reliability of climatic datasets.
Keywords:
- rainfall
- data
- messing
- values
- quality
- control
- inhomogeneities
- SNHT
How to cite
Malika EL-HAMDOUNY, Manal EL GAROUANI, Maryam EL-YAZIDI, Abdelbaset MIDAOUI, Mimoun GOUMIH & Abderrahim LAHRACH (2025). DATA QUALITY ASSESSMENT AND HOMOGENIZATION OF RAINFALL TIME SERIES IN DATA-SCARCE REGIONS: A CASE STUDY OF THE UPPER OUM ER-RBIA BASIN, NORTHERN MOROCCO, Carpathian Journal of Earth and Environmental Sciences, February 2025 Vol. 20, No. 1, p. 97 – 106; https://doi.org/10.26471/cjees/2025/020/317