Dynamically Constrained Interpolation of the Sparsely Observed Suspended Sediment Concentrations in Both Space and Time: A Case Study in the Bohai Sea

Abstract

AbstractThe observed suspended sediment concentrations (SSCs) obtained from the water sampling are usually sparsely distributed in both space and time, which are traditionally applied just to calibrate other types of observations. In this study a dynamically constrained interpolation methodology (DCIM) is developed to interpolate these sparsely observed SSCs in the Bohai Sea. In this method the suspended sediment transport model is taken as dynamical constraints to interpolate the observations. Meanwhile, the interpolated results are optimized iteratively by adjusting the key model parameters using the adjoint method.The DCIM is first verified using the synthetic observations produced by twin model runs. The modeling results reveal that this method is effective at interpolating the sparsely observed artificial SSCs, even when the observations are heavily contaminated by data noise. Then, the sparsely observed practical SSCs obtained from a large area survey in the Bohai Sea are interpolated using the DCIM. The interpolated results are verified by randomly selected independent observations. The discrepancies between the interpolated SSCs and the observations are significantly decreased. When all the observations are interpolated, the final interpolated SSCs captured a majority (96.88%) of observations with a factor of 2 and the correlation coefficient between the observed and interpolated SSCs is 0.98. Besides, the interpolated results have presented the reasonable dynamical variations of SSCs in the space and time domains. The modeling results indicate that the DCIM is an effective tool for interpolating the sparsely observed SSCs in both space and time.