Abstract

Surface water dynamics play an important role in water, energy and carbon cycles of the Amazon Basin. A macro-scale inundation scheme was integrated with a surface-water transport model and the extended model was applied in this vast basin. We addressed the challenges of improving basin-wide geomorphological parameters and river flow representation for large-scale applications. Vegetation-caused biases embedded in the HydroSHEDS DEM data were alleviated by using a vegetation height map of about 1-km resolution and a land cover dataset of about 90-m resolution. The average elevation deduction from the DEM correction was about 13.2 m for the entire basin. Basin-wide empirical formulae for channel cross-sectional geometry were adjusted based on local information for the major portion of the basin, which could significantly reduce the cross-sectional area for the channels of some subregions. The Manning roughness coefficient of the channel varied with the channel depth to reflect the general rule that the relative importance of riverbed resistance in river flow declined with the increase of river size. The entire basin was discretized into 5395 subbasins (with an average area of 1091.7 km2), which were used as computation units. The model was driven by runoff estimates of 14 years (1994 - 2007) generated by the ISBA land surface model. The simulated results were evaluated against in situ streamflow records, and remotely sensed Envisat altimetry data and GIEMS inundation data. The hydrographs were reproduced fairly well for the majority of 13 major stream gauges. For the 11 subbasins containing or close to 11 of the 13 gauges, the timing of river stage fluctuations was captured; for most of the 11 subbasins, the magnitude of river stage fluctuations was represented well. The inundation estimates were comparable to the GIEMS observations. Sensitivity analyses demonstrated that refining floodplain topography, channel morphology and Manning roughness coefficients, as well as accounting for backwater effects could evidently affect local and upstream inundation, which consequently affected flood waves and inundation of the downstream area. It was also shown that the river stage was sensitive to local channel morphology and Manning roughness coefficients, as well as backwater effects. The understanding obtained in this study could be helpful to improving modeling of surface hydrology in basins with evident inundation, especially at regional or larger scales.

Highlights

  • Some efforts were made to further address four aspects of the aforementioned challenges: (1) while alleviating the vegetation-caused biases embedded in the digital elevation model (DEM) data, we explicitly considered the spatial variability of those biases; (2) the approach for estimating channel cross-sectional dimensions was refined to improve its representation of the spatial variability in channel geometry; (3) the Manning roughness coefficient of the channel was allowed to vary with the channel depth; and (4) backwater effects were accounted for to better represent river flow in gentle-gradient reaches

  • Floodplain inundation is a key component of surface water dynamics in the Amazon Basin

  • A macroscale inundation scheme for representing floodplain inundation was incorporated into the Model for Scale Adaptive River Transport (MOSART) and the extended model was applied to the entire Amazon Basin

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Summary

Introduction

Paiva et al (2013a, b) used the full Saint-Venant equations (or the dynamic wave method) to represent water flow of river reaches with gentle riverbed slopes and large floodplains These studies showed that accounting for backwater effects could improve the modeling of surface water dynamics in this basin. The roles of the following factors in hydrologic modeling for the Amazon Basin were separately examined and demonstrated: (1) representing floodplain inundation; (2) alleviating vegetation-caused biases in the DEM data; (3) refining channel cross-sectional geometry; (4) adjusting Manning roughness coefficients; and (5) representing backwater effects.

MOSART model
Macroscale inundation scheme
Application in the Amazon Basin
Vegetation-caused biases in DEM
Channel geometry
Manning roughness coefficients for channels
Control simulation
Streamflow
River stage
Flood extent
Sensitivity study
On 2 Off 3 On 4 On 5 On 6 On 7 On
Representing floodplain inundation
Correcting DEM
Refining channel geometry
Varying Manning roughness coefficients
Backwater effects
Backwater effects on flood extent
Backwater effects on streamflow
Backwater effects on river stages
Findings
Summary and discussion
Full Text
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