Bayesian model calibration in ungauged catchments via satellite data

We launch a novel approach for hydrologic modeling in ungauged basins using satellite data. Due to universal availability, satellite data is an attractive option to fill the absence of in-situ data to calibrate a hydrologic model in ungauged or poorly gauged basins. The one specific satellite-derived calibration-measurement ratio (C/M ratio) is useful because of its physical property to indicate the water extent and its demonstrated correlation with observed streamflow. However, there are challenges in calibrating a hydrologic model using the C/M ratio because it has a different dimension to streamflow. Therefore, a new approach is required to use the modeled surrogate streamflow instead of the raw C/M ratio in place of streamflow. A new Bayesian approach is introduced here to identify parameters of surrogate streamflow in the absence of a time series of streamflow. This approach calibrates the conjugated probability of the parameter set of a hydrologic model and a surrogate streamflow model. Specifically, the proposed likelihood includes supplementary information, such as an estimated mean value of streamflow, to join the information of streamflow volume and dynamics of the modeled flow. Our new approach is assessed for multiple Australian Hydrologic Stations with distinct attributes. The strength in the new method is demonstrated with high Nash-Sutcliffe Efficiency values (0.535 ~ 0.781), and the uncertainties in the new model calibration are quantified via Markov Chain Monte Carlo sampling. The errors of the surrogate streamflow model and the hydrologic model are analyzed, and the predictive intervals are assessed with the benchmark model derived from the in-situ streamflow. Overall, our work improves previous studies on the hydrological predictions using the C/M ratio. Furthermore, it enables surrogate data to be highly correlated to the actual data, regardless of their dimensions.