Exploring Predictive Relationships of Fluvial Morphology: Using Shuttle Radar Topography Mission Data

To identify general large-scale patterns (slope, slope change, sinuosity) along a river\u27s course the worldwide SRTM 3 arc-second DEM satellite derived data was analyzed. Longitudinal profiles were calculated for sixteen rivers . This analysis uses auxiliary data sets to develop an understanding of the external and internal influences that are pressed upon and inherent within the lower 100 meters of the river systems. Contradictory to previous findings, the sixteen rivers studied here show that slope and sinuosity are not strongly correlated at the reach scale. The total river\u27s longitudinal profile up to 100 meters, provides an average slope and sinuosity throughout the entire system and increases the correlation between slope and sinuosity (~0.56). Comparing the entire river\u27s longitudinal profiles also illustrates a threshold of planform sinuosity (\u3e1.6) in which meandering rivers are found. Using this threshold, the Indus, Mississippi, and Fly Rivers are further examined to understand lateral migration rates, the link between meandering rivers and the production of oxbow lakes throughout their floodplain. The slope of three rivers was examined for external controls by overlaying geological data of bedrock type and fault locations. Neotectonics appears to impact the slope and/or sinuosity of the Mississippi, Niger, and Magdalena rivers. Results indicate growth faulting found in the mud-dominated systems of the Mississippi and Niger influences sinuosity. The resulting sinuosity is greatest in regions where these rivers are bound by growth faults. The Magdalena has several regions where the river intersects strike-slip faults, resulting in increased slopes with the more parallel the encounter. River longitudinal profiles can also reveal areas of bedload erosion and deposition. Zones of erosion (sources) and deposition (sinks), and knowing how to locate them, are of great interest to a variety of geoscientists. These predictive relationships will provide future assistance to the field of fluvial morphology