Development of a disaggregated multi-level factorial hydrologic data assimilation model

A disaggregated multi-level factorial hydrologic data assimilation model (FHDA) is proposed for exploring not only the direct effects from individual uncertainties but also, more importantly, the composite ones from multi-layer and multi-parameter interactions among multiple uncertainties in hydrologic data assimilation systems. Based on a disaggregated multi-level factorial analysis method, the proposed FHDA examined the contributions of multiple uncertainty sources, including data assimilation scheme, sample size, forcing data error, and observed data error. Three parameter assimilation schemes of data assimilation i.e., Ensemble Kalman Filter (EnKF), Standard Kernel Smoother (EnKFSKS), and Kernel Smoother with location shrinkage (EnKFKSLS) are tested. The results indicate that: i) the streamflow observations can be well tracked by 95% prediction intervals. ii) reducing streamflow observation error is the most efficient way to improve both deterministic and probabilistic predictions. iii) data assimilation scheme plays a vital role in hydrological predictions, especially for probabilistic ones (i.e., contributes 22% for the ensemble prediction uncertainty). Therefore, to improve the prediction accuracy, it is necessary to optimize the parameter assimilation schemes in hydrological data assimilation model.