Lateral cavities in streams : flow structure and mean residence times from channel hydraulics, morphology, and computational fluid dynamics Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/vx021j705

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  • Surface transient storage (STS) and hyporheic transient storage (HTS) have functional significance in stream ecology and hydrology. Both provide refugia for aquatic communities and their longer mean residence times (compared to the main flow) increase the potential for biogeochemical reactions that can improve water quality. As STS and HTS have different storage and mass exchange mechanisms, hydrologists have proposed quantitatively separating STS from HTS to better predict solute fate and transport in streams. In addition, more accurate estimates of mass exchange parameters, such as mean residence times, are needed for STS and HTS. At present, effective solute transport parameters are estimated either from empirical relationships or by parameterizing effective transport metrics in solute transport models, resulting in empirical and nontransferrable parameters and an approximate equifinality in optimized numerical solutions. Through the development of relationships using field-measureable hydraulic and morphologic parameters, transient storage mass exchange parameters can be better constrained in solute transport models. To develop mass exchange relationships for transient storage, this dissertation focuses on the study of a prevalent and widelyrecognized type of STS termed lateral cavities. Lateral cavities have flow fields characterized by a recirculation region comprised of one or more gyres and a shear layer that spans the entire entrance. The goals of this dissertation are: (1) to develop a classification scheme that categorizes different types of STS in fluvial systems in order to quantitatively separate STS from HTS; and (2) to develop accurate estimates of mass exchange parameters (i.e., mean residence times) for lateral cavities in order to better understand and quantify solute transport and dispersion in fluvial systems. There are six major contributions of this work to the hydrology community. First, to quantitatively separate STS from HTS, a fluid-mechanics-based classification scheme is presented that identifies and categorizes different types of STS based on their characteristic mean flow structure. The classification scheme will allow for the systematic study of different STS types and development of predictive mean residence time relationships. Second, the best estimate of lateral cavity mean residence time, which represents the mean residence time of the primary gyre, is the first characteristic time of exponential decay. Third, a cavity shape factor--ratio of the square root of cavity width and depth to the cavity length--represents the degree of cavity equidimensionality and best quantifies the effect of cavity shape on mean residence time. Fourth, two roughness factors have good correlations with normalized mean residence time when computed using the median grain diameter of sediments measured in the shear layer: ratio of median grain diameter to channel depth and ratio of shear velocity to mean channel velocity. Fifth, mean residence time relationships are derived for lateral cavities in open channel flows with hydraulically smooth beds and for lateral cavities in gravel-bed rivers and streams. The mean residence time relationships are applicable for lateral cavities over a range of geometry, shape, roughness, and flow conditions. Sixth, cavity configuration (e.g., series or parallel) has a greater influence on breakthrough curve shape and transport parameters than the number of lateral cavities present. Therefore, the configuration and interaction of transient storage zones must be considered to accurately quantify stream solute transport and is a missing component in current solute transport theory.
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  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2014-03-06T21:19:18Z (GMT) No. of bitstreams: 5 JacksonTracieR2014.pdf: 7976581 bytes, checksum: 9e3ade9a2698e2de36e61f97e0510408 (MD5) MicroADV.xlsx: 173009 bytes, checksum: 9011e2ab0d4851d13c1bf544a4031d00 (MD5) Bathymetry.xls: 5285888 bytes, checksum: bdd3867e882f9e09b8e18027d421055c (MD5) Velocity Profiles and Discharge.xlsx: 15747 bytes, checksum: 3075a3c2dc3289a9bcdfd610db098ccf (MD5) StereoPIV.xlsx: 612219 bytes, checksum: 3b67cae20c9db3f13651d25095873ddb (MD5)
  • description.provenance : Made available in DSpace on 2014-03-06T23:23:43Z (GMT). No. of bitstreams: 5 JacksonTracieR2014.pdf: 7976581 bytes, checksum: 9e3ade9a2698e2de36e61f97e0510408 (MD5) MicroADV.xlsx: 173009 bytes, checksum: 9011e2ab0d4851d13c1bf544a4031d00 (MD5) Bathymetry.xls: 5285888 bytes, checksum: bdd3867e882f9e09b8e18027d421055c (MD5) Velocity Profiles and Discharge.xlsx: 15747 bytes, checksum: 3075a3c2dc3289a9bcdfd610db098ccf (MD5) StereoPIV.xlsx: 612219 bytes, checksum: 3b67cae20c9db3f13651d25095873ddb (MD5) Previous issue date: 2014-02-04
  • description.provenance : Submitted by Tracie Jackson (jacksotr@onid.orst.edu) on 2014-02-15T02:56:34Z No. of bitstreams: 5 JacksonTracieR2014.pdf: 7976581 bytes, checksum: 9e3ade9a2698e2de36e61f97e0510408 (MD5) MicroADV.xlsx: 173009 bytes, checksum: 9011e2ab0d4851d13c1bf544a4031d00 (MD5) Bathymetry.xls: 5285888 bytes, checksum: bdd3867e882f9e09b8e18027d421055c (MD5) Velocity Profiles and Discharge.xlsx: 15747 bytes, checksum: 3075a3c2dc3289a9bcdfd610db098ccf (MD5) StereoPIV.xlsx: 612219 bytes, checksum: 3b67cae20c9db3f13651d25095873ddb (MD5)
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2014-03-06T23:23:43Z (GMT) No. of bitstreams: 5 JacksonTracieR2014.pdf: 7976581 bytes, checksum: 9e3ade9a2698e2de36e61f97e0510408 (MD5) MicroADV.xlsx: 173009 bytes, checksum: 9011e2ab0d4851d13c1bf544a4031d00 (MD5) Bathymetry.xls: 5285888 bytes, checksum: bdd3867e882f9e09b8e18027d421055c (MD5) Velocity Profiles and Discharge.xlsx: 15747 bytes, checksum: 3075a3c2dc3289a9bcdfd610db098ccf (MD5) StereoPIV.xlsx: 612219 bytes, checksum: 3b67cae20c9db3f13651d25095873ddb (MD5)

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