A hydrodynamic model-based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay.

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.

The Chesapeake Bay Program Modeling System: Overview and Recommendations for Future Development.

The Chesapeake Bay is the largest, most productive, and most biologically diverse estuary in the continental United States providing crucial habitat and natural resources for culturally and economically important species. Pressures from human population growth and associated development and agricultural intensification have led to excessive nutrient and sediment inputs entering the Bay, negatively affecting the health of the Bay ecosystem and the economic services it provides. The Chesapeake Bay Program (CBP) is a unique program formally created in 1983 as a multi-stakeholder partnership to guide and foster restoration of the Chesapeake Bay and its watershed. Since its inception, the CBP Partnership has been developing, updating, and applying a complex linked modeling system of watershed, airshed, and estuary models as a planning tool to inform strategic management decisions and Bay restoration efforts. This paper provides a description of the 2017 CBP Modeling System and the higher trophic level models developed by the NOAA Chesapeake Bay Office, along with specific recommendations that emerged from a 2018 workshop designed to inform future model development. Recommendations highlight the need for simulation of watershed inputs, conditions, processes, and practices at higher resolution to provide improved information to guide local nutrient and sediment management plans. More explicit and extensive modeling of connectivity between watershed landforms and estuary sub-areas, estuarine hydrodynamics, watershed and estuarine water quality, the estuarine-watershed socioecological system, and living resources will be important to broaden and improve characterization of responses to targeted nutrient and sediment load reductions. Finally, the value and importance of maintaining effective collaborations among jurisdictional managers, scientists, modelers, support staff, and stakeholder communities is emphasized. An open collaborative and transparent process has been a key element of successes to date and is vitally important as the CBP Partnership moves forward with modeling system improvements that help stakeholders evolve new knowledge, improve management strategies, and better communicate outcomes.

Detection of the effects of stormwater control measure in streams using a Bayesian BACI power analysis.

The unpredictable timing and magnitude of precipitation events and the spatiotemporal variability of constituent concentrations are major complications to effective monitoring of watershed nutrient and sediment loads. Furthermore, detecting small changes in constituent loads in response to implementation of Stormwater control measures (SCMs) against natural variability is a challenge. Nevertheless, regulatory frameworks that direct reductions of pollutants to streams frequently depend on the ability to quantify changes in loads after management interventions. The before-after-control impact (BACI) sampling design is often used to assess the effects of an environmental change made at a known point in time. However, this approach may be complicated to apply to nutrient and sediment loads in streams as the relative impact of SCMs on nutrient concentration conditional on the long term variability of discharges has not been evaluated. Multi-scale monitoring studies that provide estimates of the natural temporal and spatial variability of discharge and concentrations could provide useful information in designing a BACI study. Here we use data from the Baltimore Long Term Ecological Research (LTER) sites and urban restoration sites to develop multiple statistical measures of the effectiveness of a given monitoring scheme in revealing the hypothesized restoration effects in terms of hydrology and nutrient loads. Stratified sampling over baseflow and stormflow and the use of multiple control streams were useful tools to detect long term cumulative reductions in concentrations due to SCMs. Moderate reductions in concentration (20%), however, were not detectable with the design options considered. We emphasize that appropriate pre-planning of monitoring schemes and sampling frequency is essential to determine if the effects on constituent loads resulting from a given watershed restoration activity are measurable.

Progress and challenges in coupled hydrodynamic-ecological estuarine modeling.

Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear, because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a "theory of everything" for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

Multi-method approach to quantify uncertainties in the measurements of light absorption by particles.

Through technological and research advances, numerous methods and protocols have emerged to estimate spectral absorption of light by particles, ap, in an aquatic medium. However, the level of agreement among measurements remains elusive. We employed a multi-method approach to estimate the measurement precision of measuring optical density of particles on a filter pad using two common spectrophotometric methods, and the determination precision, or uncertainty, of the computational techniques for estimating ap for six ocean color wavelengths (412, 443, 490, 510, 555, 670 nm). The optical densities measured with the two methods exhibited a significant, positive correlation. Optical density measurement precision ranged from 0.061%-63% and exhibited a significant, positive correlation. Multi-method uncertainty ranged from 7.48%-119%. Values of ap at 555 nm and 670 nm exhibited the highest values of uncertainty. Poor performance of modeled ap compared to determined ap suggest uncertainties are propagated into bio-optical algorithms.