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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

Hammoud Yassine, Allali Youssef, Saadane Abderrahim

Reports on Geodesy and Geoinformatics

2025

Vol. 119

85--98

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.