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

Abstract

River discharge is a key variable for understanding the global hydrological cycle and assessing water resources (Oki & Kanae, 2006). Networks of in situ stream gauging stations are a fundamental data source for estimating spatial and temporal variations in the discharge of major rivers worldwide. However, a number of accessible stream gauges are not adequate to fully understand details of the global hydrological cycle, and

A Network‐Scale Modeling Framework for Stream Metabolism, Ecosystem Efficiency, and Their Response to Climate Change

“…Regarding other environmental variables, the acquisition of data in any catchment is progressively facilitated by recent methodological advances which combine satellite information with modeling, see for instance Revel et al. (2021) and Hossain et al. (2022) for streamflow discharge, Savoy et al.…”

“…To this end, a preliminary analysis could be carried out following the method proposed by Bertuzzo et al (2022), which allows partitioning the contribution of different OC sources to ER using DO time series. Regarding other environmental variables, the acquisition of data in any catchment is progressively facilitated by recent methodological advances which combine satellite information with modeling, see for instance Revel et al (2021) and Hossain et al (2022) for streamflow discharge, Savoy et al (2021) for light availability at streambed, and Bertuzzo et al (2022) for litterfall input derived from Leaf Area Index remote observation. Finally, crucial to the applicability of the presented model are long (annual or multiannual), high-frequency time series of DO, which are increasingly available thanks to the recent advent of cheap sensor technology (Bernhardt et al, 2018).…”

Hydrologic Model Parameter Estimation in Ungauged Basins Using Simulated SWOT Discharge Observations

“…Since real SWOT data are not yet available, proxy SWOT observations were generated for this analysis. Proxy SWOT data have been used by multiple studies to quantify assimilation impacts on river modeling and reservoir management (Andreadis et al., 2007; Biancamaria et al., 2011; Emery et al., 2020; Munier et al., 2015; Revel et al., 2021; Wongchuig‐Correa et al., 2020; Yang et al., 2019) and develop procedures for estimating river bathymetry (Durand et al., 2008, 2010, 2014; Yoon et al., 2012). Furthermore, Pedinotti et al.…”

“…Since real SWOT data are not yet available, proxy SWOT observations were generated for this analysis. Proxy SWOT data have been used by multiple studies to quantify assimilation impacts on river modeling and reservoir management (Andreadis et al, 2007;Biancamaria et al, 2011;Emery et al, 2020;Munier et al, 2015;Revel et al, 2021;Wongchuig-Correa et al, 2020;Yang et al, 2019) and develop procedures for estimating river bathymetry (Durand et al, 2008(Durand et al, , 2010(Durand et al, , 2014Yoon et al, 2012). Furthermore, Pedinotti et al (2014) used synthetic SWOT data to optimize Manning roughness coefficients in the Interactions between Soil, Biosphere, and Atmosphere-Total Runoff Integrating Pathways System (ISBA-TRIP) continental hydrologic system using data assimilation, demonstrating that SWOT data can be used for calibration.…”

Correction of River Bathymetry Parameters Using the Stage–Discharge Rating Curve

“…The correction of bathymetry parameters in large‐scale hydrodynamic models is a widely studied problem in hydrology. Bathymetry is typically corrected based on riverbed elevation bias (Revel et al., 2020); this process relies on observed WSEs (synthetic values or in situ and satellite‐based observations) and high‐quality model simulations of water depth. However, in many studies, simulated river discharge contained errors (Moramarco et al., 2019; Oubanas et al., 2018), resulting in errors in subsequent water depth simulations and river bathymetry correction.…”