Improving parameter and state estimation of a hydrological model with the ensemble square root filter

Data assimilation techniques are widely used in hydrology and water resources management to improve model forecast uncertainty by assimilating observations. The big challenge practical applications is how describe uncertainties correctly avoid the occurrence of spurious covariance during data assimilation. In this study, ensemble square root filter (EnSRF) estimate parameters states a groundwater Guantao, China, which updates means perturbations separately avoids need perturb decreased with time while However, incorrect were obtained, could not be corrected further observations improving representation hydrological system. To compensate for effect reduce other sampling errors introduced assimilation, localization two covariance-tuning methods (inflation factor damping factor) explored study. results show that alternative scenarios proper length or large inflation small produce better estimates performance. scenario 0.05 shows distinct gain predictive capability. method superior preferable real field applications. combining improved performance EnSRF respect different amounts measurement error also analysed. Even though increase observation can covariance, corresponding improvement observed as case, less informative.