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Different estimates of the regional water vapor scale height, taken from ERA5 reanalysis, in situ observations and the direct optimization of retrieved water vapor profiles in GNSS tomography, are found to have major impact in the performance of tomographic inversions, with the better results displaying mean errors comparable to radiosondes. The analysis uses 7 months of GNSS (Global Navigation Satellite Systems) observations in the Amazon Dense GNSS Network near Manaus, Brazil, in 2011–2012, to compute a time series of water vapor profiles, with a tomographic technique capable of producing quasi‐instantaneous inversions with minimal external data or constraints. Results compare very well with 12‐hourly in situ radiosondes, especially in the lower troposphere above 2 km, and its daily‐to‐seasonal variability compares well with WRF (Weather Research and Forecasting) convective‐permitting simulations driven by ERA5 boundary conditions, suggesting that GNSS tomography may be an important source of atmospheric water vapor data for different applications. Plain Language Summary: Global Navigation Satellite Systems data from GPS, retrieved in the Amazon rainforest near Manaus during a 7‐month period, are used to compute time‐continuous vertical profile of water vapor above the rainforest by tomographic inversion, providing a unique view of the variability of the water vapor field in this crucial sector of the South American climate system. Variability from subdaily to seasonal time scales is clearly present, with a strong signature of the transition into the wet season of the South American monsoon. Key Points: Water vapor tomography in the central Amazon captures diurnal‐to‐seasonal variability and the transition to the wet seasonTomography outperforms atmospheric reanalysis in the lower troposphere and may constitute a source of high temporal resolution dataAn estimate of the regional water vapor scale height is found to be crucial to the accuracy of the tomographic inversion [ABSTRACT FROM AUTHOR]

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Copyright of Rodriguésia is the property of Revista Rodriguesia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the geomorphic classification and bathymetry (at 100 m) of 12 major floodplains along a 900 km reach of the middle-lower Amazon River (Manaus – Monte Alegre; total floodplain area of 5164 km2), based on the inundation frequency (IF) derived from 36 yr of Landsat data. Using a mathematical relationship between IF and surveyed depth at the Curuai floodplain in June 2004, this IF-depth model is applied across floodplains of the middle-lower Amazon to estimate bathymetry. The applicability of the model is justified by similar grain size distribution (dominantly silt and clay) of sediment in the surface water of floodplain lakes (the main materials constructing the floodplains), as well as a hydrogeomorphic classification of this reach: seven floodplains lying on the alluvial plain dominated by the Amazon-flood pulse (Type I), three floodplains incised in valleys dominated by sediment-poor upland tributaries (Type II) and two floodplains of mixed types. This classification is used to distinguish the sensitivity of floodplains to the IF-depth model, where Type I floodplains of higher sensitivity provide a more accurate depth estimation. The bathymetry for both types is validated with field survey data across eight floodplains (covering over 200 km survey distance) collected in June – July 2016 and adjusted by the difference between mean June 2004 and June/July 2016 water level. Correlation coefficients of 0.87 (Type I) and 0.94 (Type II) indicate a strong relationship between estimated and measured depth for both floodplain types, while the root mean square error of 1.03 m (Type I) and 1.02 m (Type II) suggest that bathymetry is estimated to around 1 m error, equivalent to 10.9% and 10.8% of maximum water level variability, respectively. We offer the first field-validated and complete bathymetry map for the middle-lower Amazon River with accuracy that can be used to evaluate the role of floodplains in influencing biodiversity, sedimentation, flood control and biogeochemical cycling. • First field-validated floodplain bathymetry of the middle lower Amazon River • Depth estimation is based on inundation frequency and field survey data. • Floodplain classification based on morphology, geology and flooding dynamics [ABSTRACT FROM AUTHOR]