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Title: Observing water level dynamics in the Amazon using satellite altimetry
Author: Hall, Amanda Christine
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2013
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The River Amazon has the largest drainage basin in the world; however, spatial and temporal water fluxes in the floodplain are poorly understood. With few in situ gauging stations, all situated on the main channel, understanding flow dynamics of the floodplain is difficult. The work in this thesis uses ICESat (Ice, Cloud and land Elevation Satellite) to observe changes in water level in the channel and floodplain. To ensure the suitability of ICESat for this purpose, a proof-of-concept study was conducted on the Mississippi and Danube rivers. This study found that ICESat was capable of accurately observing water levels (mean bias and standard deviation of -16 ± 16 cm). These results allowed ICESat to be used with confidence in the Amazon. The Amazon main channel gauges are not tied to a common datum. ICESat was used to level six gauges within the study area to a common datum to be used as verification data. With the aid of Landsat imagery, ICESat was used to investigate the floodplain, identifying water level dynamics in both floodplain lakes and channels with unprecedented detail. Seasonal changes in water level, as well as spatial patterns, were clearly observable. Water levels in the main channel and floodplain were compared to ENVISAT altimeter data and showed good agreement, except at low water when the greatest difference was seen. The ICESat data were then used to create maps of water flow directions and gradients across the floodplain. These ICESat-derived flow characteristics were used to assess simulations from the hydrodynamic model LISFLOOD-FP and identify whether the model is able to reproduce observed floodplain water levels, as well as determining possible causes for discrepancy. Overall, detailed analysis of ICESat data has provided unprecedented detail of water fluxes in the Amazon, advancing understanding of the dynamics of large basin floodplain inundation processes.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available