Author(s): Hao Lin; Carlo Gualtieri; Hualong Luan; Haiyu Yuan
Linked Author(s):
Keywords: Mazon basin; Eco-hydraulics; Spatial habitat metrics; Entropy theory; Surface velocity
Abstract: In a changing environment, enhanced knowledge of Eco-hydraulics is vital for understanding the river ecosystems. In river ecosystems, the aquatic habitats are closely related to hydrodynamic characteristics such as gradients in velocity. Hydraulic complexity metrics M1 and M2 are useful for analysing the aquatic habitats. Parameters M1 and M2 refer to how much drag an organism applies after moving between two locations. The above analysis is based on flow velocities from the in-situ measurements. However, the method is time-consuming and costly, and poses significant safety hazards for hydrographers during high floods. Thus, the present research examines the application of entropy model to estimate the hydraulic complexity metrics just using the surface-velocity measurements. The flow velocity data collected by ADCP from the gauged site Jatuarana station in the Amazon basin were used. A coupling model based on the Entropy theory and ecological metrics estimation is established. First, by analysing all surface velocity measurements across each transect, the Entropy concept is used to estimate the cross-sectional velocity distribution. The computed velocities match well with the field observations for all flow conditions. Then, based on the velocity distribution derived by Entropy, hydraulic complexity parameters are estimated. The results showed that the metrics exhibit variability across the transect. Larger M1 values are found near the center of the transect, while lower ones are found at the bank sides. Larger M2 values are observed near the bed of the channel, suggesting locations with larger kinetic energy consumption for aquatic organisms. This research highlights the possibility of evaluating the ecological patterns using non-contact measurements of surface velocity in river flows. Based on the Entropy concept, these findings could be especially useful in understanding biologically important flow patterns within different rivers.
Year: 2024