Water balance data are affected by various errors (bias and noise). To reduce these errors, this study presents a water balance data fusion approach that combines multi-scale data (from satellites and in-situ sensors) for each water balance variable and jointly calibrates them, resulting in consistent, bias-corrected and noise-filtered, water balance estimates, along with uncertainty bands. These estimates are useful for constraining process-based models and informing water management decisions.