ORIGINAL ARTICLE
Improving TerraClimate hydroclimatic data accuracy with XGBoost for regions with sparse gauge networks: A case study of the Meknes plateau and the Middle Atlas Causse, Morocco
1
Centre of Water, Natural Resources, Environment, and Sustainable Development (CERNE2D), Faculty of Sciences, Mohammed V University, 4 Avenue Ibn Batouta BP 1014 RP, Rabat, Morocco
These authors had equal contribution to this work
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2024-08-21
Final revision date: 2025-03-12
Acceptance date: 2025-03-19
Publication date: 2025-04-25
Corresponding author
Yassine Hammoud
Centre of Water, Natural Resources, Environment, and Sustainable Development (CERNE2D), Faculty of Sciences, Mohammed V University, 4 Avenue Ibn Batouta BP 1014 RP, Rabat, Morocco
Centre of Water, Natural Resources, Environment, and Sustainable Development (CERNE2D), Faculty of Sciences, Mohammed V University, 4 Avenue Ibn Batouta BP 1014 RP, Rabat, Morocco
Reports on Geodesy and Geoinformatics 2025;119:85-98
KEYWORDS
TOPICS
ABSTRACT
Access to reliable hydroclimatic data, including precipitation, temperature, evapotranspiration, and runoff is crucial for effective water resource management, especially in water-stressed regions like Morocco. However, the scarcity of meteorological stations makes data collection difficult. Satellite products offer a promising alternative to these stations for monitoring and forecasting hydroclimatic trends. This study focuses on the Meknes Plateau and the Middle Atlas Causse to assess the reliability of TerraClimate data and explore their optimization using the XGBoost Machine Learning algorithm. Comparative evaluation between measured data and raw TerraClimate data reveals a satisfactory correlation, though data accuracy imperfections persist. Applying the XGBoost algorithm significantly improves the raw TerraClimate data, reducing the average Mean Absolute Error (MAE) across all parameters from 3.08 to 0.29, and the average Root Mean Square Error (RMSE) from 4.84 to 0.46, and increasing the average Nash-Sutcliffe Efficiency (NSE) from 0.82 to 0.99. These improvements validate this approach in enhancing hydroclimatic data quality in the studied region. In conclusion, this study highlights the potential of satellite products, especially TerraClimate, combined with optimization techniques, for example, the XGBoost algorithm, to address hydroclimatic data shortages in water-stressed regions. The results constitute a robust foundation for future initiatives aimed at improving water resource management and resilience to water challenges in Morocco.
ACKNOWLEDGEMENTS
We would like to extend our sincere gratitude to the TerraClimate team for providing their dataset, which was essential for the completion of this study. Additionally, we wish to thank the members of the Research Team and our Supervisor for their invaluable support and guidance throughout this project.
FUNDING
This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
CONFLICT OF INTEREST
The authors declare that there is no conflict of interest regarding the publication of this paper.
DATA AVAILABILITY
The data used in this study were accessed from two primary sources: (1) observed hydroclimatic data (precipitation, temperature, evapotranspiration, and runoff) collected from eight meteorological stations in the Meknes Plateau and Middle Atlas Causse, provided by the Sebou Hydraulic Basin Agency (ABHS) and the Provincial Directorate of Agriculture (DPA) of Meknes, and (2) TerraClimate satellite data, which were made available by the TerraClimate dataset. The observed station data are available upon request from the respective agencies, while the TerraClimate dataset can be accessed online at https://www.climatologylab.org/terraclimate.html or through Google Earth Engine. Processed data and scripts used for analysis in this study are available from the corresponding author upon reasonable request.
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