Characterising errors using satellite metadata for eco-hydrological modelling

Understanding the origin of errors in model predictions is a critical element in hydrologic model calibration and uncertainty estimation. While there exist a variety of plausible error sources, only one measure of the total residual error can be ascertained when the observed response is known. Here we show that collecting extra information a priori to characterise the data error before calibration can assist in improved model calibration and uncertainty estimation. A new model calibration strategy using the satellite metadata information is proposed as a means to inform the model prior, and subsequently to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that 1) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; 2) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance ; 3) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.