The Effects of Tidal Forcing on Nutrient Fluxes in the Tidal, Freshwater James River Estuary, VA

A 12-month study (January to December 2015) focused on the effects of tidal forcing on nutrient fluxes in the tidal, freshwater segment of the James River Estuary (JRE). Discrete sampling of nutrient chemistry and continuous monitoring of tidal discharge were used to determine the volume and timing of the tides, and differences in nutrient concentrations between incoming and outgoing tides. The goal of this study was to improve understanding of tidal influence on nutrient fluxes and their role in nutrient transport to the lower estuary. Results suggested that differences in nutrient concentrations between incoming and outgoing tides were small throughout the year. This finding suggests that nutrient fluxes at the study site, near the tidal fresh-oligohaline boundary of the James, are largely determined by tidal volume owing to weak concentrations gradients. Changes in water quality during seaward and landward tidal excursions into deeper versus shallower segments were analyzed to infer biogeochemical processes. Differences in oxygen production and nitrate utilization suggest greater autotrophy during landward excursions, consistent with more favorable light conditions. This work was conducted as a collaborative effort between Virginia Commonwealth University, the USGS, Randolph-Macon College, and Washington and Lee University participating in the “Mountains to the Sea” project

Water quality modeling as an inverse problem

An inverse mathematical estuarine eutrophication model has been developed. The model provides a framework to estimate unknown parameters by assimilation of the concentration data of those state variables. The inverse model developed is a laterally integrated, two-dimensional, real-time model which consists of a hydrodynamic model, an eutrophication model and an adjoint model. The hydrodynamic model provides the dynamic fields for both the eutrophication model and the adjoint model. The eutrophication model simulates eight water quality state variables which are phytoplankton, organic nitrogen, ammonium nitrogen, nitrite-nitrate nitrogen, organic phosphorus, inorganic (ortho) phosphorus, carbonaceous biochemical oxygen demand and dissolved oxygen. The adjoint model is used during the processes of parameter estimation to provide the gradients of the cost function with respect to the unknown parameters. to increase the computational efficiency and reduce computer storage space, a decoupling scheme is implemented in the inverse model, in which the kinetic processes are decoupled from the physical transport for the purpose of numerical computation. An efficient preconditioning technique is introduced in the inverse model to speed up the rate of convergence. The experiments conducted in this study provide the information of the parameter identifiability and the field data requirement for the model calibration. The model experiments with hypothetical data sets show that the parameters can be accurately estimated for short period and long period model simulations under both constant and time-varying boundary conditions. The inverse model is convergent with different initial guess parameter values and under different environments. The inverse model was successfully applied to aid calibration of the eutrophication model of the tidal Rappahannock River, Virginia. With the use of the inverse model, the eutrophication model can be calibrated efficiently and systematically. The agreement between the model predictions and observations are very satisfactory. The studies show that the inverse model is also useful in addressing the important questions of whether the estimated parameters are unique and whether the sample data are sufficient to calibrate a model. Therefore, the inverse model may also serve as a tool in helping design a field program to collect data for model calibration

Influence of salinity on SAV distribution in a series of intermittently connected coastal lakes

Intermittently closed and open lakes and lagoons (ICOLLs) are coastal lakes that intermittently exchange water with the sea and experience saline intrusions. Understanding effects of seawater exchange on local biota is important to preserve ecosystem functioning and ecological integrity. Coastal dune lakes of northwest Florida are an understudied group of ICOLLs in close geographic proximity and with entrance regimes operating along a frequency continuum. We exploited this natural continuum and corresponding water chemistry gradient to determine effects of water chemistry on resident submersed aquatic vegetation (SAV) distributions in these ecosystems. SAV distribution decreased with increases in salinity, but was unaffected by variation in nitrogen, phosphorous, and turbidity. Salinity perturbations corresponding with water exchange with the Gulf of Mexico were associated with reductions in SAV in coastal dune lakes. Potential impacts associated with changes in global climate may increase the frequency of seawater exchange across all coastal dune lakes and potentially reduce the distribution of oligohaline macrophytes among these ecosystems

Quantifying sediment resuspension and internal phosphorus loading in shallow near-shore areas in the Gulf of Finland

Peer reviewe

Spatiotemporal Estuarine Water Quality Parameterization Using Remote Sensing and in-situ Characteristics

This dissertation develops a new paradigm in a water quality monitoring approach to parameterize spatiotemporal estuarine water quality with sustainable reliability, less cost and less time. A key underpinning of this paradigm of the spatiotemporal estuarine water quality parameterization is various water quality parameters\u27 interrelationship with ambient water temperature as a common factor, their time dependent characteristics, and spatiotemporal characteristics of remote sensing. It has two core models to provide input data of water quality parameterization model in a system; the transfer function models of the physical system and an analytical temperature time series model. The objective of this dissertation is to provide an alternative tool for monitoring water quality and decision-making in estuaries with time and space, to identify system components contributing to physical water quality, and to demonstrate the feasibility, reproducibility and applicability of the proposed model. The spatiotemporal estuarine water quality parameterization model monitors chlorophyll concentration using remote sensing, transfer function models of dissolved oxygen (DO) and orthophosphate (PO4) and ambient water temperature in spring and fall in the James River Estuary Mesohaline segment in Virginia. The proposed model is applicable in the temperature range between 6°C and 23°C in spring and in the temperature range between 21°C and 32°C in fall. The optimal operational temperature range of the proposed model is between 19°C and 25°C based on the relative sensitivity analysis of DO transfer function model. The proposed models in two seasons are compared with the models that use different approaches such as a conventional approach and a previously proposed approach based on various criteria. The results show that the proposed models present the variability of chlorophyll concentration better over time and temperature than other approaches. The results also support that the transfer function models can be successfully applied to estimate chlorophyll instead of using monitored water quality data directly. The proposed models present difficulty to estimate extremely high concentrations of chlorophyll; however, they produce estimations comparable to observed chlorophyll concentrations that are less than the extreme outliers in each season. The mean chlorophyll concentration that is produced by the best proposed model is 7.937μg/L and the +/- 95% confidence intervals of the mean are 7.977μg/L and 7.897μg/L after eliminating the extreme outliers (371μg/L) in spring. The mean, 7.937μg/L, is compatible with the mean of the observed concentrations that are less than the extreme outliers, 7.572μg/L. The mean chlorophyll concentration that is produced by the best proposed model is 5.520μg/L, and the +/- 95% confidence intervals (C.I.) of the mean are 5.538μg/L and 5.502μg/L after eliminating the extreme outliers (22μg/L) in fall. The mean, 5.520μg/L, is compatible with the mean of the observed concentrations that are less than the extreme outliers, 6.117μg/L. This dissertation demonstrates the feasibility, reproducibility and applicability of the paradigm in spatiotemporal estuarine water quality parameterization using remote sensing data and field measured water quality data in estuaries. The spatiotemporal estuarine water quality parameterization model can enhance an existing water quality monitoring and assessment program in estuaries that are managed by municipal agencies and local water quality decision makers. The spatiotemporal estuarine water quality parameterization model can be employed as a tool to guide management, since a systematic process of estimating water quality targets is difficult in a complex estuary. Over time, the model provides appropriate, up-to-date guidance. Careful consideration is necessary when applying transfer function models and seasonal spatiotemporal estuarine water quality parameterization models to the different estuaries directly. Although the models appear feasible with significant potential, direct implementation of the model requires a site-specific quality assurance/quality control (QA/QC) check

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay

Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid- 1980