The Development and Proliferation of Summer Algal Blooms in the Oligo/Poly-Haline Portion of the Chesapeake Bay - Observational and Numerical Modeling Studies

Algal blooms occur annually in many parts of the Chesapeake Bay. The causes of algal blooms are complex and can be different in different regions. In this study, we will conduct data analysis for the observed data and adopt various methods to investigate algal bloom phenomenon in three separate regions in the oligo/poly-haline portion of the Bay. Chapter 1 provides a general introduction of the algal bloom research in the Chesapeake Bay. In Chapter 2, an observational analysis and a numerical study on the algal blooms in Back River were conducted. A hypothesis was made that high pH can trigger sediment phosphorus release, which in turn can enhance chlorophyll-a and further increase pH to form a positive feedback loop. to test this theory, water quality model ICM coupled onto SCHISM was applied in Back River to study the phenomenon. Moreover, a pH model was developed to describe the aquatic chemistry. The model results with and without pH model were compared with Bay Program observations for verifying our hypothesis. It proves the importance of sediment phosphorus release on the algal blooms in Back River. In Chapter 3, a theoretical study combined with data analysis on cyanobacteria blooms dynamics was conducted in the upper tidal James River. The theory integrates the physical transport and biological effects, which leads to a simple governing equation composed of an advection term and a phytoplankton net growth term, in both linear and nonlinear forms. In this study, we derived a general analytic solution to the equation. Then, we applied the theory in the tidal freshwater portion of the James River. The theoretical predictions of chlorophyll concentrations were compared with observational data and verified the validity of the solution. In addition, the factors related to the local chlorophyll maximum in tidal freshwater rivers were discussed. In Chapter 4, an observational analysis and numerical experiments were performed to investigate the algal bloom in the polyhaline of the Chesapeake Bay. This exploratory study is aimed to explain the broad distribution of C. polykrikoides blooms in the lower Bay and the sudden disappearance of the bloom in 2014. A hypothesis is made regarding the origin of C. polykrikoides cysts. In this hypothesis, the cysts are considered to be originated from coastal ocean and their transport is under the influence of wind patterns and gravitational circulation. In this study, the hydrodynamics in the lower Chesapeake Bay was first analyzed. Then, a series of particle tracking experiments were conducted for investigating the physical transport of C. polykrikoides cysts under different environmental conditions. Finally, water quality model ICM was used to simulate the algal blooms caused by C. polykrikoides in the lower Bay by incorporating the biological features of C. polykrikoides. The model can generate reasonable magnitude of the algal blooms in 2012, 2013 and simulate no algal bloom condition in 2014.The result indicates that C. polykrikoides cysts could be originated from the coastal ocean, while temperature and wind patterns play important roles in further controlling the subsequent development of the blooms

Quantifying consensus of rankings based on q-support patterns

Rankings, representing preferences over a set of candidates, are widely used in many information systems, e.g., group decision making and information retrieval. It is of great importance to evaluate the consensus of the obtained rankings from multiple agents. An overall measure of the consensus degree provides an insight into the ranking data. Moreover, it could provide a quantitative indicator for consensus comparison between groups and further improvement of a ranking system. Existing studies are insufficient in assessing the overall consensus of a ranking set. They did not provide an evaluation of the consensus degree of preference patterns in most rankings. In this paper, a novel consensus quantifying approach, without the need for any correlation or distance functions as in existing studies of consensus, is proposed based on a concept of q-support patterns of rankings. The q-support patterns represent the commonality embedded in a set of rankings. A method for detecting outliers in a set of rankings is naturally derived from the proposed consensus quantifying approach. Experimental studies are conducted to demonstrate the effectiveness of the proposed approach

Assessment of Hydrodynamic and Water Quality Impacts for Channel Deepening in the Thimble Shoals, Norfolk Harbor, and Elizabeth River Channels : Final report on the “hydrodynamic modeling”

For over twenty years, the U. S. Army Corps of Engineers (USACE) and the Virginia Port Authority (VPA), representing the Commonwealth Secretary of Transportation, have collaborated on projects key to port development that also preserve the environmental integrity of both Hampton Roads and the Elizabeth River. The USACE and the VPA are working to investigate channel deepening in this region to provide access to a new generation of cargo ships (e.g., Panamax-class). The main goal of this project is to investigate the feasibility for Norfolk Harbor channel deepening in the lower James and Elizabeth Rivers and assess the environmental impact of the shipping channels dredging in Atlantic Ocean Channel, Thimble Shoal Channel, Elizabeth River channel, and the Southern Branch. Specifically, we support the request of “Planning and Engineering Services for Norfolk Harbor” in three areas: (1) using high-resolution hydrodynamic modeling to evaluate the change of hydrodynamics resulting from Channel Deepening (2) assessment of water quality modeling using the Hydrodynamic Eutrophication Model (HEM3D) (3) conducting the statistical measure of impacts resulting from Channel Deepening. Virginia Institute of Marine Science (VIMS) team has applied a3D unstructured-grid hydrodynamic model (SCHISM, Zhang et al., 2016) in the study of impact of channel dredging on hydrodynamics in the project area. The model was adopted due to its flexible gridding systems used: hybrid triangular-quadrangular unstructured grids in the horizontal and flexible vertical coordinate system in the vertical (Zhang et al. 2015). High resolution (up to 15m) is used to faithfully resolve the channels and other important features such as tunnel islands, etc

Light Regulation of Phytoplankton Growth in San Francisco Bay Studied Using a 3D Sediment Transport Model

In San Francisco Bay (SFB), light availability is largely determined by the concentration of suspended particulate matter (SPM) in the water column. SPM exhibits substantial variation with time, depth, and location. To study how SPM influences light and phytoplankton growth, we coupled a sediment transport model with a hydrodynamic model and a biogeochemical model. The coupled models were used to simulate conditions for the year of 2011 with a focus on northern SFB. For comparison, two simulations were conducted with ecosystem processes driven by SPM concentrations supplied by the sediment transport model and by applying a constant SPM concentration of 20 mg l1. The sediment transport model successfully reproduced the general pattern of SPM variation in northern SFB, which improved the chlorophylla simulation resulting from the biogeochemical model, with vertically integrated primary productivity varying greatly, from 40 g[C] m2 year1 over shoals to 160 g[C] m2 year1 in the deep channel. Primary productivity in northern SFB is influenced by euphotic zone depth (Ze). Our results show that Ze in shallow water regions (\u3c2 \u3em) is mainly determined by water depth, while Ze in deep water regions is controlled by SPM concentration. As a result, Ze has low (high) values in shallow (deep) water regions. Large (small) differences in primary productivity exist between the two simulations in deep (shallow) water regions. Furthermore, we defined a new parameter Flight for “averaged light limitation” in the euphotic zone. The averaged chlorophyll-a concentration in the euphotic zone and Flight share a similar distribution such that both have high (low) values in shallow (deep) water regions. Our study demonstrates that light is a critical factor in regulating the phytoplankton growth in northern SFB, and a sediment transport model improves simulation of light availability in the water column

Wind-Modulated Western Maine Coastal Current and Its Connectivity With the Eastern Maine Coastal Current

Using a high-resolution circulation model and an offline particle tracking model, we investigated variations of the Western Maine Coastal Current (WMCC) and its connectivity with the Eastern Maine Coastal Current (EMCC). The models showed that the weak, broad, and sinuous WMCC is generally southwestward with an offshore and a nearshore core, fed by the extension of the EMCC and runoff from the Penobscot and Kennebec–Androscoggin Rivers, respectively. A sea-level dome can form offshore of Casco Bay in late fall and early winter as the northeastward alongshore wind sets up a seaward sea-level gradient from the coast to meet the shoreward sea-level gradient from Wilkinson Basin. Consequently, northeastward flows (i.e., the counter-WMCC) emerge on the inshore side of the dome. Both the circulation and particle tracking models suggested that the connectivity generally peaks twice annually, highest in winter and then secondarily in late spring or early summer. The former is concurrent with the most southwest offshore veering of the EMCC, while the latter is concurrent with the strongest EMCC. Moreover, the counter-WMCC can reduce the connectivity and result in year-to-year variations

Tidal wind mapping from observations of a meteor radar chain in December 2011

This article proposes a technique to map the tidal winds in the mesosphere and lower thermosphere (MLT) region from the observations of a four-station meteor radar chain located at middle- and low-latitudes along the 120 degrees E meridian in the Northern Hemisphere. A 1month dataset of the horizontal winds in the altitude range of 80-100km is observed during December 2011. We first decompose the tidal winds into mean, diurnal, semidiurnal, and terdiurnal components for each station. It is found that the diurnal/semidiurnal components dominate at the low-latitude/midlatitude stations. Their amplitudes increase at lower altitudes and then decrease at higher altitudes after reaching a peak in the MLT region. Hough functions of the classical tidal theory are then used to fit the latitudinal distribution of each decomposed component. The diurnal component is found to be dominated by the first symmetric (1, 1) mode. Yet for the semidiurnal and terdiurnal components, the corresponding dominant modes are the second symmetric modes (2, 4) and (3, 5), and considerable contributions are also from the first antisymmetric modes (2, 3), (3, 4) and second antisymmetric modes (2, 5), (3, 6). Based on the decomposed results, we further map the horizontal winds in the domains of latitude, altitude and local time. The mapped horizontal winds successfully reproduce the local time versus altitudinal distributions of the original observations at the four stations. Thus, we conclude that the meteor radar chain is useful to monitor and study the regional characteristics of the tidal winds in the MLT region

Cross-Scale Baroclinic Simulation of the Effect of Channel Dredging in an Estuarine Setting

Holistic simulation approaches are often required to assess human impacts on a river-estuary-coastal system, due to the intrinsically linked processes of contrasting spatial scales. In this paper, a Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) is applied in quantifying the impact of a proposed hydraulic engineering project on the estuarine hydrodynamics. The project involves channel dredging and land expansion that traverse several spatial scales on an ocean-estuary-river-tributary axis. SCHISM is suitable for this undertaking due to its flexible horizontal and vertical grid design and, more importantly, its efficient high-order implicit schemes applied in both the momentum and transport calculations. These techniques and their advantages are briefly described along with the model setup. The model features a mixed horizontal grid with quadrangles following the shipping channels and triangles resolving complex geometries elsewhere. The grid resolution ranges from similar to 6.3 km in the coastal ocean to 15 m in the project area. Even with this kind of extreme scale contrast, the baroclinic model still runs stably and accurately at a time step of 2 min, courtesy of the implicit schemes. We highlight that the implicit transport solver alone reduces the total computational cost by 82%, as compared to its explicit counterpart. The base model is shown to be well calibrated, then it is applied in simulating the proposed project scenario. The project-induced modifications on salinity intrusion, gravitational circulation, and transient events are quantified and analyzed

FedALA: Adaptive Local Aggregation for Personalized Federated Learning

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.Comment: Accepted by AAAI 202

FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

Recently, personalized federated learning (pFL) has attracted increasing attention in privacy protection, collaborative learning, and tackling statistical heterogeneity among clients, e.g., hospitals, mobile smartphones, etc. Most existing pFL methods focus on exploiting the global information and personalized information in the client-level model parameters while neglecting that data is the source of these two kinds of information. To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively. FedCP is more fine-grained to consider personalization in a sample-specific manner than existing pFL methods. Extensive experiments in computer vision and natural language processing domains show that FedCP outperforms eleven state-of-the-art methods by up to 6.69%. Furthermore, FedCP maintains its superiority when some clients accidentally drop out, which frequently happens in mobile settings. Our code is public at https://github.com/TsingZ0/FedCP.Comment: Accepted by KDD 202

GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities. Recently, personalized FL (pFL) has received attention for its ability to address statistical heterogeneity and achieve personalization in FL. However, from the perspective of feature extraction, most existing pFL methods only focus on extracting global or personalized feature information during local training, which fails to meet the collaborative learning and personalization goals of pFL. To address this, we propose a new pFL method, named GPFL, to simultaneously learn global and personalized feature information on each client. We conduct extensive experiments on six datasets in three statistically heterogeneous settings and show the superiority of GPFL over ten state-of-the-art methods regarding effectiveness, scalability, fairness, stability, and privacy. Besides, GPFL mitigates overfitting and outperforms the baselines by up to 8.99% in accuracy.Comment: Accepted by ICCV202