Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco

Domaine:

climateenvironment and energy

Type de record:

paper

Créateur:

ChaAbaRodLaf

Éditeur:

UniUniAgrUni

Éditeur:

CCSD

Hôte:

International audience Accurate precipitation data are essential for effective water resource management. This study aimed to correct precipitation values from the ERA5_Ag reanalysis dataset using observational data from 20 meteorological stations located in the Tensift basin, Morocco. Five machine learning models were evaluated: MLP, XGBoost, CatBoost, LightGBM, and Random Forest. Model performance was assessed using RMSE, MAE, R², and bias metrics, enabling the selection of the best-performing model to apply the correction. The results showed significant improvements in the accuracy of precipitation estimates, with R² ranging between 0.80 and 0.90 in most stations. The best model was subsequently used to correct and generate raster maps of corrected precipitation over 42 years, providing a spatially detailed tool of great value for water resource management. This study is particularly important in semi-arid regions such as the Tensift basin, where water scarcity demands more accurate and informed decision-making.

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Spatial Bias Correction of ERA5_Ag Reanalysis Precipitation Using Machine Learning Models in Semi-Arid Region of Morocco

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