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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.

High-Resolution Lithology Detection Using Sentinel-2A, ALOS PRISM L1B Images, and Support-Vector Machines in Tagragra d’Akka Inlier of Western Anti-Atlas, Morocco

Hammoud Yassine, Allali Youssef, Saadane Abderrahim

Geomatics and Environmental Engineering

2025

Vol. 19, no. 1

103--141

Geological mapping faces substantial challenges due to inaccessible terrains, labor-intensive field methods, and potential interpretative errors. This study proposes an innovative approach that leverages automatic lithology classification using multispectral Sentinel-2A (10 m) and high-resolution panchromatic ALOS PRISM L1B (2.5 m) images. Applied to the Tagragra d’Akka inlier of the Anti-Atlas region, the methodology enhances spatial resolution through pansharpening, followed by unsupervised segmentation. The segmented images are classified using support vector machines (SVMs) (supervised learning algorithms) to distinguish the lithological units. Achieving an 86% overall accuracy and an 84% kappa coefficient, the approach demonstrated robust performance and surpassed conventional techniques. The integration of machine learning and remote sensing offers a promising frontier for geological mapping – particularly in regions like the Tagragra d’Akka inlier. This study marks a significant advancement in automating lithological mapping, with implications for geological research, resource management, and hazard assessment. Automated techniques in geological cartography significantly enhance mapping accuracy and efficiency. Future studies should explore additional data sources and machine-learning algorithms to refine lithological classification and validate these methods across diverse geological settings.