Assimilation of Satellite Altimetry Data for Effective River Bathymetry

Abstract

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.

The Applicability of SWOT’s Non-Uniform Space–Time Sampling in Hydrologic Model Calibration

“…Hydrologic and hydrodynamic models are useful for assessing climate change impacts [1], predicting flood characteristics, forecasting applications [2], and understanding the transfer and storage of water and energy globally [3]. Satellite remote sensing has been utilized to quantify and assess water security [4], and measurements can be coupled with hydrologic models to improve overall discharge estimation [5][6][7][8][9]. In many modeling applications, calibration methods are used to optimize model parameters for estimating river discharge [10][11][12][13][14].…”

A Framework for Estimating Global‐Scale River Discharge by Assimilating Satellite Altimetry

“…Recently, global‐scale river‐width datasets have been developed using high‐resolution satellite images (Allen & Pavelsky, 2015; Yamazaki et al., 2014). Methods to estimate channel bathymetric depth using SWOT observations have also been proposed (Brêda et al., 2019; Durand et al., 2008; Revel et al., 2018; Yoon et al., 2012). Estimating other hydrodynamic parameters, such as Manning's coefficient, is also essential and may be achieved using SWOT observations (Emery et al., 2020a; Pedinotti et al., 2014).…”

Integrating Lateral Inflows Into a SWOT Mission River Discharge Algorithm

“…Hydrologic modeling is one method to determine river discharge; large‐scale global models have the capability to produce discharge estimations (Lohmann et al, 2004; Wada et al, 2016; Wisser et al, 2010; Yamazaki et al, 2011). Modeled discharge can also be used in tandem with satellite remote sensing to improve overall discharge estimations (Brêda et al, 2019; Gleason et al, 2018; Liu et al, 2015); discharge algorithms used in remote sensing methods often use mean annual flow estimates derived from models (Durand et al, 2008; Durand, Gleason, Garambois, et al, 2016) for their prior estimations. Remote sensing methods for calculating river discharge have become increasingly popular (Bjerklie et al, 2003; Gleason et al, 2018; Smith & Pavelsky, 2008; Syed et al, 2009; Tarpanelli et al, 2018, 2019), and thus, we look to improve methods of discharge estimation in relation to the upcoming Surface Water and Ocean Topography (SWOT) satellite mission developed jointly by NASA and CNES, with participation from the Canadian and UK space agencies.…”