GSA Annual Meeting, November 5-8, 2001

Paper No. 0
Presentation Time: 1:30 PM-5:30 PM

ENERGY EXPENDITURE OF RIVER NETWORKS IN HYDROCLIMATICALLY EXTREME ENVIRONMENTS


HOWE, Susan R., Earth Resources, Colorado State Univ, 128 Pearl Street, Fort Collins, CO 80521 and WOHL, Ellen E., Colorado State Univ, Dept Earth Resources, Fort Collins, CO 80523-1482, showe@lamar.colostate.edu

Optimal Channel Network Theory is explored for its applicability to riverine systems in hydroclimatically extreme environments. Optimal Channel Networks are obtained by minimizing the rate of total energy expenditure in a river system, and some combination of energy minimization and uniformity at the reach scale. River network parameters, including hydraulic geometry and bed slope, are extracted from a digital elevation model of the Upper Rio Chagres, Panama, and are used to estimate annual total and unit energy expenditure. Values are compared against a modeled Optimal Channel Network whose characteristics reflect both uniformity and minimization of energy expenditure throughout the basin. Deviations within the Charges basin from its modeled optimal network are identified and discussed as potential areas of instability. The Upper Rio Chagres basin spans an area of approximately 466 sq km, with approximately 0.85 km of relief. The network is a seventh order Strahler system, predominantly pool-riffle and step-pool bedrock-controlled channels, with an annual average discharge and sediment of approximately 8.2E8 m3/yr and 2.55 ton/ha, respectively, since 1981. Extreme fluctuations in discharge are typical, ranging from > than 500m3/sec to <20m3/sec during high flow periods (Oct-Dec), and >100m3/sec and <2m3/sec during low flows (Feb-Mar). Therefore, channel adjustments in areas identified as unstable are likely to occur during extreme events, primarily as localized vertical erosion and deposition due to lateral constriction of most channel reaches by bedrock. Limitations of Optimal Channel Network models and suggestions for improving their predictive capacity in network evolution are discussed.