Stage‐discharge rating curves based on satellite altimetry and modeled discharge in the Amazon basin

Abstract

In this study, rating curves (RCs) were determined by applying satellite altimetry to a poorly gauged basin. This study demonstrates the synergistic application of remote sensing and watershed modeling to capture the dynamics and quantity of flow in the Amazon River Basin, respectively. Three major advancements for estimating basin-scale patterns in river discharge are described. The first advancement is the preservation of the hydrological meanings of the parameters expressed by Manning's equation to obtain a data set containing the elevations of the river beds throughout the basin. The second advancement is the provision of parameter uncertainties and, therefore, the uncertainties in the rated discharge. The third advancement concerns estimating the discharge while considering backwater effects. We analyzed the Amazon Basin using nearly one thousand series that were obtained from ENVISAT and Jason-2 altimetry for more than 100 tributaries. Discharge values and related uncertainties were obtained from the rain-discharge MGB-IPH model. We used a global optimization algorithm based on the Monte Carlo Markov Chain and Bayesian framework to determine the rating curves. The data were randomly allocated into 80% calibration and 20% validation subsets. A comparison with the validation samples produced a Nash-Sutcliffe efficiency (E ns ) of 0.68. When the MGB discharge uncertainties were less than 5%, the E ns value increased to 0.81 (mean). A comparison with the in situ discharge resulted in an E ns value of 0.71 for the validation samples (and 0.77 for calibration). The E ns values at the mouths of the rivers that experienced backwater effects significantly improved when the mean monthly slope was included in the RC. Our RCs were not mission-dependent, and the E ns value was preserved when applying ENVISAT rating curves to Jason-2 altimetry at crossovers. The cease-to-flow parameter of our RCs provided a good proxy for determining river bed elevation. This proxy was validated against Acoustic Doppler current profiler (ADCP) cross sections with an accuracy of more than 90%. Altimetry measurements are routinely delivered within a few days, and this RC data set provides a simple and cost-effective tool for predicting discharge throughout the basin in nearly real time.

Toward continental hydrologic–hydrodynamic modeling in South America

“…On the other hand, even if discharges are well represented, there are uncertainties related to Manning values and also to river widths and 25 depths derived from HGs, which do not reflect singularities of cross sections such as narrowing or widening of rivers at both gauge and VS locations. Previous studies have demonstrated the large influence of channel geometry and roughness on both amplitude and timing of water levels, especially over the Amazon basin (e.g., Yamazaki et al, 2011;Paiva et al, 2013;Paris et al, 2016;Luo et al, 2017). Moreover, river bed profiles are subject to DEM errors that can hardly be reduced through simple profile-smoothing procedures.…”

River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage

“…; Paris et al. ; Garambois et al. ), these methods do not extract information from the actual terrain.…”

“…While prior research using remotely sensed data or statistical approaches to investigate river geometry and rating curves has resulted in useful insights and approaches (Getirana et al 2013;Paris et al 2016;Garambois et al 2017), these methods do not extract information from the actual terrain. Relationships obtained from statistical analyses are thus insufficient to depict the unique physiographic characteristics of each channel and remotely sensed imagery only captures part of a transient state of the river system from a top view, which is incomplete.…”

Assimilation of Satellite Altimetry Data for Effective River Bathymetry

“…Satellite altimetry has already been used for assessing and improving hydrological models. For example, altimetry data were applied on model validation (Neal et al, ; Paiva, Collischonn, et al, ), calibration of water budget parameters (Getirana, ), assimilation for estimating discharge fields (Paiva et al, ), and development of stage‐discharge rating curves (Paris et al, ).…”