资源环境科技发展态势分析平台

One of the main problems of hydrologic/hydrodynamic routing models is defining the right set of parameters, especially on inaccessible and/or large basins. Remote sensing techniques provide measurements of the basin topography, drainage system, and channel width; however current methods for estimating riverbed elevation are not as accurate. This paper presents methods of altimetry data assimilation (DA) for estimating effective bathymetry of a hydrodynamic model. We tested past altimetry observations from satellites ENVISAT, ICESAT, and JASON 2 and synthetic altimetry data from satellites ICESAT 2, JASON 3, SARAL, and Surface Water and Ocean Topography to assess future/present mission's potential. The DA methods used were direct insertion, linear interpolation, the Shuffled Complex Evolution-University of Arizona optimization algorithm, and an adapted Kalman filter developed with hydraulically based variance and covariance introduced in this paper. The past satellite altimetry DA was evaluated comparing simulated and observed water surface elevation while the synthetic altimetry DA were assessed through a direct comparison with a true bathymetry. The Shuffled Complex Evolution-University of Arizona and hydraulically based Kalman filter methods presented the best performances, reducing water surface elevation error in 65% in past altimetry data experiment and reducing biased bathymetry error in 75% in the synthetic experiment; however, the latter method is much less computationally expensive. Regarding satellites, it was observed that the performance is related to the satellite intertrack distance, as higher number of observation sites allows more accurate bed elevation estimation.