A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX)

Key Points

• Water storage anomalies were effectively identified using a machine learning approach.
• The method analyzed data spanning over four decades, starting in 1980.
• Temporal extension of water storage data was utilized to enhance accuracy in anomaly detection.
• This analysis may enable better resource management using historical water storage data.