Thesis advisor: Noah P. Snyder / Though previous studies have shown saltmarsh adaptability to some degree of sea-level rise (SLR), sediment supply is critical to sustaining saltmarshes as SLR accelerates. Land-use activities, such as dams, often influence watershed sediment transport and delivery to the coast. Previous studies have suggested that, even in small watersheds, dams can significantly impact coastal sediment budgets. The Parker River watershed (PRW) in northeastern Massachusetts hosts 20 dams and several natural lakes, and drains into the Plum Island Sound Estuary (PIE). This research aims to evaluate the impact of dams and sediment transport in the PRW. Three approaches were used: theoretical modeling of sediment transport patterns using digital elevation models; spatial analysis of suspended sediment concentration (SSC) and remote sensing data; and empirical calculations of reservoir trap efficiency. Geomorphic modeling indicates that bankfull discharge can transport 20 μm grains (silt) as wash load throughout the PRW. Sediment deposition might happen at Crane Pond and in reservoirs, but removing dams would not change this pattern. Both SSC data and observations of satellite images during high-flow events indicate low supply and transport of sediment throughout the PRW. The estimates of sediment yield (Y) are low for the PRW. An empirical calculation indicates little-to-no trap efficiencies for all dams. Therefore, fluvial contribution to the sediment budget of the PIS estuary is limited and dam removals in the PRW are unlikely to change the rate of sediment delivery to the PIE. The proposed method of this study provides an additional scope to assess the ecological benefits of removing a dam and could be easily replicated for other locations for similar assessment. Future studies should assess sediment dynamics and management practices from a more thorough perspective incorporating the riverine, estuarine and shelf system. / Thesis (MS) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Earth and Environmental Sciences.

Yes / This paper reviews existing studies relating to the assessment of sediment concentration profiles within various flow conditions due to their importance in representing pollutant propagation. The effects of sediment particle size, flow depth, and velocity were considered, as well as the eddy viscosity and Rouse number influence on the drag of the particle. It is also widely considered that there is a minimum threshold velocity required to increase sediment concentration within a flow above the washload. The bursting effect has also been investigated within this review, in which it presents the mechanism for sediment to be entrained within the flow at low average velocities. A review of the existing state-of-the-art literature has shown there are many variables to consider, i.e., particle density, flow velocity, and turbulence, when assessing the suspended sediment characteristics within flow; this outcome further evidences the complexity of suspended sediment transport modelling.

Yes / The transfer of suspended sediment can range widely from being diluted to being hyperconcentrated, depending on the local flow and ground conditions. Using the Rouse model and the Kundu and Ghoshal (2017) model, it is possible to look at the sediment distribution for a range of hyper-concentrated and diluted flows. According to the Kundu and Ghoshal model, the sediment flow follows a linear profile for the hyper-concentrated flow regime and a power law applies for the dilute concentrated flow regime. This paper describes these models and how the Kundu and Ghoshal parameters (linear-law coefficients and power-law coefficients) are dependent on sediment flow parameters using machine-learning techniques. The machine-learning models used are XGboost Classifier, Linear Regressor (Ridge), Linear Regressor (Bayesian), K Nearest Neighbours, Decision Tree Regressor, and Support Vector Machines (Regressor). The models were implemented on Google Colab and the models have been applied to determine the relationship between every Kundu and Ghoshal parameter with each sediment flow parameter (mean concentration, Rouse number, and size parameter) for both a linear profile and a power-law profile. The models correctly calculated the suspended sediment profile for a range of flow conditions ( 0.268 𝑚𝑚𝑚𝑚 ≤ 𝑑𝑑50 ≤ 2.29 𝑚𝑚𝑚𝑚, 0.00105 𝑔𝑔 𝑚𝑚𝑚𝑚3 ≤ particle density ≤ 2.65 𝑔𝑔 𝑚𝑚𝑚𝑚3 , 0.197 𝑚𝑚𝑚𝑚 𝑠𝑠 ≤ 𝑣𝑣𝑠𝑠 ≤ 96 𝑚𝑚𝑚𝑚 𝑠𝑠 , 7.16 𝑚𝑚𝑚𝑚 𝑠𝑠 ≤ 𝑢𝑢∗ ≤ 63.3 𝑚𝑚𝑚𝑚 𝑠𝑠 , 0.00042 ≤ 𝑐𝑐̅≤ 0.54), including a range of Rouse numbers (0.0076 ≤ 𝑃𝑃 ≤ 23.5). The models showed particularly good accuracy for testing at low and extremely high concentrations for type I to III profiles.

Yes / During flooding, the suspended sediment transport usually experiences a wide-range of dilute to hyper-concentrated suspended sediment transport depending on the local flow and ground con-ditions. This paper assesses the distribution of sediment for a variety of hyper-concentrated and dilute flows. Due to the differences between hyper-concentrated and dilute flows, a linear-power coupled model is proposed to integrate these considerations. A parameterised method combining the sediment size, Rouse number, mean concentration, and flow depth parameters has been used for modelling the sediment profile. The accuracy of the proposed model has been verified against the reported laboratory measurements and comparison with other published analytical methods. The proposed method has been shown to effectively compute the concentration profile for a wide range of suspended sediment conditions from hyper-concentrated to dilute flows. Detailed com-parisons reveal that the proposed model calculates the dilute profile with good correspondence to the measured data and other modelling results from literature. For the hyper-concentrated profile, a clear division of lower (bed-load) to upper layer (suspended-load) transport can be observed in the measured data. Using the proposed model, the transitional point from this lower to upper layer transport can be calculated precisely.