Field Validation of DEM- and GIS-Derived Longitudinal Stream Profiles

Longitudinal stream profiles provide valuable information concerning geomorphic features and energy states in a stream. Traditionally, stream profiles have been generated by field surveying or topographic map analysis. The continued growth of digital data and Geographic Information Systems (GISs) provide another method by which to achieve stream profile generation. This work examines the effectiveness of digital data, digital elevation models (DEM), and GIS to construct stream profiles. To determine the most effective and accurate data for profile generation, profiles were created using 1-meter (1-m) and 3-meter (3-m) DEMs developed from LiDAR data. Additionally, stream profiles were created from unfilled DEMs and filled DEMs to determine the need to correct potential errors in the DEMs. Thirty-three stream longitudinal profiles were created using GIS, with six segments verified by field surveys. Filled DEMs were found to remove actual features; thus, the filled DEMs were used in subsequent analyses. No significant differences between profiles generated from 1-m and 3-m were observed. Stream profiles constructed from unfilled, 3-m DEMs were similar to profiles generated from field surveyed data, although elevation differences were noted

Investigating Thermal Controls on the Hyporheic Flux as Evaluated Using Numerical Modeling of Flume-Derived Data

The flux of water through the hyporheic zone (HZ) is controlled by stream bedforms, sinuosity, surface water velocity, local water table, seasonality, and hydraulic conductivity (K) of the bed material. Dependent on both the kinematic viscosity and density of water, K values are a function of temperature. In most studies, changes in temperature have been neglected because of the limited effect either density or viscosity has on K values. However, these variations are important given the role of K in HZ flux, which lead to the hypothesis that flow into the HZ would be more efficient (faster rate and greater depth) under warmer conditions than under cool conditions. To discern how water temperature affects flow depth in the HZ, VS2DHI simulations were created to map flow under both warm and cool thermal conditions. The models employed data collected from a series of varying temperature hydrologic flume tests in which the effects of hyporheic flow altering variables such as sinuosity, surface water velocity and volume, and bed-forms were controlled. Results verify that K values in the HZ were larger under warm conditions generating deeper HZ pathways, while the smaller K values under cool conditions produced shallower pathways. The simulations confirmed a faster speed of frontal movement under warm conditions than cool. Péclet numbers revealed a shallower advective extinction depth under cool conditions as opposed to warm

Investigating Thermal Controls on the Hyporheic Flux as Evaluated Using Numerical Modeling of Flume-Derived Data

The flux of water through the hyporheic zone (HZ) is controlled by stream bedforms, sinuosity, surface water velocity, local water table, seasonality, and hydraulic conductivity (K) of the bed material. Dependent on both the kinematic viscosity and density of water, K values are a function of temperature. In most studies, changes in temperature have been neglected because of the limited effect either density or viscosity has on K values. However, these variations are important given the role of K in HZ flux, which lead to the hypothesis that flow into the HZ would be more efficient (faster rate and greater depth) under warmer conditions than under cool conditions. To discern how water temperature affects flow depth in the HZ, VS2DHI simulations were created to map flow under both warm and cool thermal conditions. The models employed data collected from a series of varying temperature hydrologic flume tests in which the effects of hyporheic flow altering variables such as sinuosity, surface water velocity and volume, and bed-forms were controlled. Results verify that K values in the HZ were larger under warm conditions generating deeper HZ pathways, while the smaller K values under cool conditions produced shallower pathways. The simulations confirmed a faster speed of frontal movement under warm conditions than cool. Péclet numbers revealed a shallower advective extinction depth under cool conditions as opposed to warm