Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river

Abstract

Abstract. Quantifying continental-scale river discharge is essential for understanding the terrestrial water cycle, but it is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation (WSE), water surface slope, river width, and flood extent). However, directly comparing simulated WSE to satellite altimetry data remains challenging (e.g., because of large biases between simulations and observations or uncertainties in parameters), and large errors can be introduced when satellite observations are assimilated into hydrodynamic models. In this study we performed direct, anomaly, and normalized value assimilation experiments to investigate the capacity of DA to improve river discharge within the current limitations of hydrodynamic modeling. We performed hydrological DA using a physically based empirical localization method applied to the Amazon basin. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2. Direct DA was the baseline assimilation method and was subject to errors due to biases in the simulated WSE. To overcome these errors, we used anomaly DA as an alternative to direct DA. We found that the modeled and observed WSE distributions differed considerably (e.g., differences in amplitude, seasonal flow variation, and a skewed distribution due to limitations of the hydrodynamic models). Therefore, normalized value DA was performed to improve discharge estimation. River discharge estimates were improved at 24 %, 38 %, and 62 % of stream gauges in the direct, anomaly, and normalized value assimilations relative to simulations without DA. Normalized value assimilation performed best for estimating river discharge given the current limitations of hydrodynamic models. Most gauges within the river reaches covered by satellite observations accurately estimated river discharge, with the Nash–Sutcliffe efficiency (NSE) > 0.6. The amplitudes of WSE variation were improved in the normalized DA experiment. Furthermore, in the Amazon basin, normalized assimilation (median NSE =0.50) improved river discharge estimation compared to open-loop simulation with the global hydrodynamic model (median NSE =0.42). River discharge estimation using direct DA methods was improved by 7 % with calibration of river bathymetry based on NSE. The direct DA approach outperformed the other DA approaches when runoff was considerably biased, but anomaly DA performed best when the river bathymetry was erroneous. The uncertainties in hydrodynamic modeling (e.g., river bottom elevation, river width, simplified floodplain dynamics, and the rectangular cross-section assumption) should be improved to fully realize the advantages of river discharge DA through the assimilation of satellite altimetry. This study contributes to the development of a global river discharge reanalysis product that is consistent spatially and temporally.

AltiMaP: altimetry mapping procedure for hydrography data

“…Thus, this elevation difference may be used as a proxy to interpret bias between simulated and observed WSEs (Fassoni-Andrade et al, 2021). Satellite altimetry data are also extremely useful for evaluating and calibrating hydrodynamic models (e.g., Zhou et al, 2022) and correcting variables through data assimilation (e.g., Revel et al, 2023b), which requires correct VS allocation to a river network map. The river bathymetry parameter can be calibrated using the rating curve method developed using satellite altimetry and in situ river discharge data (Zhou et al, 2022).…”

“…Satellite altimetry observations have been applied in several large-scale studies to monitor natural water resources in rivers and lakes (e.g., Asadzadeh Jarihani et al, 2013;Birkett et al, 2002;Calmant and Seyler, 2006;Dettmering et al, 2020;Schneider et al, 2017;Xiang et al, 2021), calibrate or validate hydrological/hydrodynamic models (e.g., Elmer et al, 2021;Jiang et al, 2019Jiang et al, , 2021Kittel et al, 2021;Meyer Oliveira et al, 2021;Zhou et al, 2022) and to assimilate data into hydrological/hydrodynamic models (e.g., Brêda et al, 2019;Michailovsky et al, 2013;Paiva et al, 2013;Revel et al, 2023b). However, incorrect VS allocation can lead to the degradation of post-calibration model performance.…”

Satellite video remote sensing for estimation of river discharge

“…Previous approaches to satellite-based river discharge monitoring typically rely upon various statistical and hydraulic approximations to make indirect estimates of river discharge from space. Popular amongst these methods, satellite radar altimetry measures water elevations at virtual river cross-sections (Revel et al ., 2023;Tarpanelliet al ., 2013;Zakharova et al ., 2020) and near-simultaneous optical imagery infers water surface flow velocity from space (Kääb et al ., 2019). Other satellite approaches have relied on remote sensing of discharge (RSQ) algorithms, which retrieve hydraulic variables such as stage from remotely sensed data and then relate these quantities to river discharge (Q ) to supplement gauge networks using hydrological models and data assimilation techniques (e.g., Gleason and Durand, 2020;Riggs et al ., 2022).…”

Satellite Video Remote Sensing for Estimation of River Discharge

“…However, satellite-based remote sensing approaches to monitoring discharge are helping to alleviate these issues (e.g., Sichangi et al, 2016).Approaches to satellite-based river discharge monitoring typically rely upon statistical and hydraulic approximations to make indirect estimates of river discharge. Widely applied satellite radar altimetry measures water elevations at virtual river cross-sections (Revel et al, 2023;Tarpanelli et al, 2013;Zakharova et al, 2020) and near-simultaneous optical imagery can be used to infer water surface flow velocity from space (Kääb et al, 2019). Other satellite approaches have relied on remote sensing of discharge (RSQ) algorithms, which retrieve hydraulic variables from remotely sensed data and then relate these quantities to river discharge (Q) (e.g., Gleason & Durand, 2020;Riggs et al, 2022).…”