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Sources and routing of the Amazon River Flood Wave
Author(s) -
Richey Jeffrey E.,
Mertes Leal A. K.,
Dunne Thomas,
Victoria Reynaldo L.,
Forsberg Bruce R.,
Tancredi Antônio C. N. S.,
Oliveira Eurides
Publication year - 1989
Publication title -
global biogeochemical cycles
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.512
H-Index - 187
eISSN - 1944-9224
pISSN - 0886-6236
DOI - 10.1029/gb003i003p00191
Subject(s) - hydrograph , floodplain , tributary , hydrology (agriculture) , channel (broadcasting) , flood myth , routing (electronic design automation) , environmental science , amazon rainforest , drainage basin , water storage , water level , main river , geology , oceanography , geography , electrical engineering , inlet , biology , cartography , geotechnical engineering , archaeology , engineering , computer science , computer network , ecology
We describe the sources and routing of the Amazon River flood wave through a 2000‐km reach of the main channel, between São Paulo de Olivença and Obidos, Brazil. The damped hydrograph of the main stem reflects the large drainage basin area, the 3‐month phase lag in peak flows between the north and south draining tributaries due to seasonal differences in precipitation, and the large volume of water stored on the floodplain. We examined several aspects of the valley floor hydrology that are important for biogeochemistry. These include volumes of water storage in the channel and the floodplain and the rates of transfer between these two storage elements at various seasons and in each segment of the valley. We estimate that up to 30% of the water in the main stem is derived from water that has passed through the floodplain. To predict the discharge at any cross section within the study reach, we used the Muskingum formula to predict the hydrograph at downriver cross sections from a known hydrograph at upstream cross‐sections and inputs and outputs along each reach. The model was calibrated using three years of data and was successfully tested against an additional six years of data. With this model it is possible to interpolate discharges for unsampled times and sites.