Bedload Transport Sampling, Characterization and Modeling on a Southern Appalachian Ridge and Valley Stream

Estimates of bedload transport rates developed from existing transport models are notoriously inaccurate(Wilcock 2001). The gravel bed models addressed in this study include the Meyer-Peter and Muller; Parker, Klingeman, and McLean; and Wilcock two-fraction models. The question of whether or not these models predict bedload transport rates in a Southern Appalachian Ridge and Valley stream is complicated by the fact that these models have only been previously assessed in terms of their agreement with bedload transport rates measured in the Western regions of the U.S. Further, due to the strongly non-linear form of bedload transport models discrete errors and cumulative uncertainty in input parameters can result in excessive error and uncertainty in results. The research presented in this dissertation approaches these issues through introduction of a new bedload transport data set collected on Little Turkey Creek in Farragut, Tennessee using a continuously monitoring bedload collection station with estimated collection efficiencies of nearly 100%. Use of 20-liter pail pit samplers is addressed for estimating bedload particle size distributions and transport model calibration. Finally, the issue of error and uncertainty in model input parameters is addressed through evaluation of the results of discrete error and cumulative uncertainty within the region of observed variation in bedload transport observations. The results of this research suggest similarity between bedload transport characteristics in Southern Appalachian Ridge and Valley streams and those of streams in the Western region of the U.S. It was found that 20-liter pail pit traps are suitable for collection of bedload transport particle size distribution data and only marginally well suited for model calibration. It was illustrated that selected bedload transport models are most sensitive to errors in estimates of Manning’s n and slope. Further, it was found that uniform uncertainty of more than 20% in model input parameters produces results that are at the outer edge of the observed variation in bedload transport rates. The body of work presented in this dissertation is intended to provide stream restoration design professionals with additional background to inform bedload transport estimates on streams in the Southern Appalachian Ridge and Valley Region

The Inverse Application of Conformal Mapping Techniques to Describe Groundwater Flow-Regimes through a Window in the Upper Claiborne Confining Layer

The purpose of this study was to establish an inverse algorithm to solve the analytic element groundwater modeling equations, developed by Anderson (2001), for state parameters based on head data from an appropriate field site. The analytical element model (AEM) equations developed by Anderson (2001) are a complex variable technique to describe flow regimes through a gap in a confining layer that otherwise separates two confined aquifers. Anderson’s equations are based on the assumptions that hydraulic conductivity is constant in the respective confined aquifers. It also assumes a hydraulic conductivity of zero for the confining layers in the system. A Levenberg-Marquardt based inverse algorithm was developed and applied to synthetic data created by the forward application of Anderson’s AEM equations based on state variables similar to those presented in the literature (Anderson 2001). The inverse algorithm was used to solve for the state parameters describing window length (L) and flux through the window (Q) given four head values observed in the forward solution. The inverse algorithm successfully predicted values for window length and flux through the window within 20% of the values used to create the synthetic head data. A study on the effect of an added observation point in the flow field was also performed. It was observed that an added observation point in the flow field resulted in better approximations of L and Q by the inverse algorithm. The algorithm was then applied to an actual field case, the Shelby Farms Site in Memphis, Tennessee, in an attempt to predict the window extent and flux through the window based on head observations from four wells installed within the window. Based on data from three separate occasions, the algorithm produced a value for window length of L=573.9 ft and flow through a unit slice of the window of Q= -525.0 ft3/day, which compares well with the value of 35,627 ft3/day for the entire window profile from other recent studies at the Shelby Farms Site

HST imaging and Keck Spectroscopy of z~6 I-band Drop-Out Galaxies in the ACS GOODS Fields

We measure the surface density of i'-band dropout galaxies at z~6 through wide field HST/ACS imaging and ultra-deep Keck/DEIMOS spectroscopy. Using deep HST/ACS SDSS-i' (F775W) and SDSS-z' (F850LP) imaging from GOODS-N (200 arcmin^2), we identify 9 i'-drops satisfying an (i'-z')_AB>1.5 selection criterion to a depth of z'_AB=25.6 (corresponding to L*_UV at z~3-4). We use HK' imaging data to improve the fidelity of our sample, discriminating against lower redshift red galaxies and cool Galactic stars. Three i'-drops are consistent with M/L/T dwarf stars. We present ultra-deep Keck/DEIMOS spectroscopy of 10 objects from our combined GOODS-N and GOODS-S i'-drop sample. We detect Lyman-alpha emission at z=5.83 from one object in the GOODS-S field, which lies only 8arcmin away (i.e. 3Mpc/h_70) from the z=5.78 object already confirmed by Bunker et al. (2003). One possible Lyman-alpha emitter at z=6.24 is found in the GOODS-N field (although identification of this spatially-offset emission line is ambiguous). Using the rest-frame UV continuum from our 6 candidate z~6 galaxies from the GOODS-N field, we determine a lower limit to the unobscured volume-averaged global star formation rate at z~6 of (5.4+/-2.2)x10^-4 h_70 M_sun/yr/Mpc^3. We find that the cosmic star formation density in galaxies with unobscured star formation rates 15M_sun/yr/h_70^2 falls by a factor of 8 between z~3 and z~6. Hence the luminosity function of LBGs must evolve in this redshift interval: a constant integrated star formation density at $z>3$ requires a much steeper faint-end slope, or a brighter characteristic luminosity. This result is in agreement with our previous measurement from the Chandra Deep Field South (Stanway et al. 2003), indicating that cosmic variance is not a dominant source of uncertainty.Comment: to appear in ApJ; replaced with accepted versio