GRACEfully Closing the Water Balance: A Data-Driven Probabilistic Approach Applied to River Basins in Iran

To fully benefit from remotely sensed observations of the terrestrial water cycle, bias and random errors in these data sets need to be quantified. This paper presents a Bayesian hierarchical model that fuses monthly water balance data and estimates the corresponding data errors and error-corrected water balance components (precipitation,...