Recent Changes in Nitrogen Sources and Load Components to Estuaries of the Contiguous United States

Monitoring data compilations can be leveraged to highlight relationships between estuarine and watershed factors influencing eutrophication in estuaries

Estuaries have been adversely impacted by increased nutrient loads. Eutrophication impacts from these loads include excess algal blooms and low oxygen conditions. In this study, we leveraged data from 28 monitoring programs in the northeastern US to explore the relationships between eutrophication response variables and watershed and estuarine variables. Extensive effort was needed to locate, harmonize, and assure the quality of the data. Random forest regression allowed us to identify the most important variables that could predict summer total nitrogen (TN), chlorophyll (chl), and bottom dissolved oxygen (DO). Several different summaries of the data were assessed. The best models for TN and chl used data summarized by estuary and year, explaining > 70% and > 60% of the variation, respectively. The best model for DO used data that were averaged by estuary across all years and explained > 55% of the variation. All models showed the importance of variables related to nutrient loading, such as population density and % development, and variables related to flushing rate, such as tidal range, length:width at mouth, and estuary openness. Future work will examine the impacts of climate on eutrophication response variables. This study demonstrates the utility of combining data from multiple unrelated routine monitoring programs to understand eutrophication impacts at regional scales.

Deciphering patterns in whole fish nitrogen isotopes on a continental scale

Nitrogen isotopes (δ15N) have been used as an indicator of anthropogenic nitrogen loading at local and regional scales. We examined δ15N in fish from estuaries across the continental United States. In the summer of 2015, the U.S. Environmental Protection Agency's National Coastal Condition Assessment (NCCA) collected fish in 136 coastal waterbodies throughout the United States. Whole fish were analyzed by NCCA for metals, organic contaminants, and lipids. For this study, we also analyzed these fish for isotopes of nitrogen (N). NCCA collected water quality, nutrients, chlorophyll a, and sediment chemistry at each site. We used these data, along with fish life history and watershed land use, to examine how whole fish δ15N was related to these environmental variables using random forest regression models at national and ecoregional scales. At the national scale, fish δ15N were negatively related to total N:total phosphorous (P) ratios (TN:TP) in surface water and reflected differences between the P-limited, δ15N depleted sites in the Floridian ecoregion to sites in other regions. δ15N was lower on the Atlantic relative to the Pacific coast. When considered by region, TN:TP was an important predictor of fish δ15N in 4 of 9 ecoregions, with higher δ15N observed with increasing N limitation (lower TN:TP) Fish life history was also an important predictor of fish δ15N at both the national and ecoregional scale. Whole fish δ15N was positively associated with bioaccumulative contaminants such as PCBs and mercury. Although land use was related to δ15N in fish, it was location specific. This study showed that N stable isotopes reflected ecological conditions at both regional and continental scales.

Assessing the relative importance of stressors to the benthic index, M-AMBI: An example from U.S. estuaries

M-AMBI, a multivariate benthic index, has been used by European and American (U.S.) authorities to assess estuarine and coastal health and has been used in scientific studies throughout the world. It has been shown to be related to multiple pressures and stressors, but the relative importance of individual stressors within a multiple stressor context has not generally been assessed. In this study, we assembled data collected between 1999 and 2015 by the U.S. Environmental Protection Agency using consistent methods. These data included sediment and water quality measures and benthic invertebrate data which were used to calculate M-AMBI. We further assembled watersheds for all US estuaries with benthic data and calculated land use metrics. Random forest (RF) was used to identify those variables most strongly related to M-AMBI. Because RF is a compilation of multiple, nonlinear models, we then assessed which of these variables had a direct relationship with M-AMBI. The resulting variables were then assessed using RF to identify the subsets of variables that produced an effective and parsimonious model. This process was conducted at the national and ecoregional scale and the variables identified as being most important to predict M-AMBI were compared with literature reports of ecological patterns in a given area. At the national scale, better condition was correlated with clearer waters, lower amounts of agriculture in the watershed, and lower carbon and metal concentrations in estuarine sediments. Other stressors were identified as being important at the ecoregional scale, although sediment metal concentrations and watershed agriculture were identified as being important in most ecoregions. Our results suggest that this technique is useful to identify the most important variables impacting M-AMBI at broad spatial scales, even when the percentage of sites in Bad or Poor condition is low. This technique also provides an initial identification of important stressors that can be used to target more intensive local studies.

Spatial and temporal variations of estuarine stratification and flushing time across the continental U.S

Estuarine circulation attributes such as stratification and flushing time significantly influence estuarine ecological processes. Stratification reflects how much vertical mixing occurs in an estuary, while flushing time can describe the exchange rate of pollutants between the estuary and ocean. A recently developed estuarine characterization framework used estuarine geophysical attributes and water exchange datasets to characterize estuarine circulation for 360 estuaries in the continental U.S. between 1950 and 2015. The estuaries were grouped into nine ecoregions according to the Marine Ecoregions of the World. In the Gulf of Mexico and along the East Coast, most estuaries were well-mixed (63-93%), with 3-5% strongly-stratified estuaries. Along the West Coast, strongly-stratified estuaries dominated (46-63%), with the exception of the Puget Trough basin and the southern CA ecoregion with 83% and 75% well-mixed estuaries. The stratification type of some estuaries varied seasonally. Generally, they were more stratified winter through spring, then mixed during the summer, with the exception of southern FL, which had a reverse pattern due to the positive correlation between the stratification parameter and freshwater inflow (97% estuaries with R 2 > 0.9). The flushing times of the 300 well-mixed and partially-stratified estuaries were estimated using Tidal Prism (TPM) and Freshwater Fraction Methods (FFM). Flushing time seasonal variation exhibited a negative correlation with freshwater inflow (R 2 > 0.8 for 50% of estuaries using TPM). Generally, estuarine flushing times were short in winter and long in summer (reversed in FL and a portion of the Gulf of Mexico). On the West Coast, estuaries tended to flush quickly compared with estuaries in other regions, even though they usually had low freshwater inflows, since other factors, e.g., the estuarine volume, affected the flushing time as well. To ensure appropriate interpretation of responses to change in nutrient loading, the significant intra- and interannual variations in stratification and flushing time need to be incorporated into management and assessment of estuaries.

Ammonium Nitrogen Streamflow Transport Modelling and Spatial Analysis in Two Chinese Basins

Ammonium nitrogen (NH4+-N), which naturally arises from the decomposition of organic substances through ammonification, has a tremendous influence on local water quality. Therefore, it is vital for water quality protection to assess the amount, sources, and streamflow transport of NH4+-N. SPAtially Referenced Regressions on Watershed attributes (SPARROW), which is a hybrid empirical and mechanistic modeling technique based on a regression approach, can be used to conduct studies of different spatial scales on nutrient streamflow transport. In this paper, the load and delivery of NH4+-N in Poyang Lake Basin (PLB) and Haihe River Basin (HRB) were estimated using SPARROW. In PLB, NH4+-N load streamflow transport originating from point sources and farmland accounted for 41.83% and 32.84%, respectively. In HRB, NH4+-N load streamflow transport originating from residential land and farmland accounted for 40.16% and 36.75%, respectively. Hence, the following measures should be taken: In PLB, it is important to enhance the management of the point sources, such as municipal and industrial wastewater. In HRB, feasible measures include controlling the domestic pollution and reducing the usage of chemical fertilizers. In addition, increasing the vegetation coverage of both basins may be beneficial to their nutrient management. The SPARROW models built for PLB and HRB can serve as references for future uses for different basins with various conditions, extending this model’s scope and adaptability.

Estimate of nutrient sources and transport into Bohai Bay in China from a lower plain urban watershed using a SPARROW model

In the development of a land-sea coordination management strategy, it is necessary to analyze pollution sources and loads of pollutants entering the sea. This study estimated the sources and transport of total nitrogen (TN) and total phosphorus (TP) entering Bohai Bay in Tianjin, a lower plain urban watershed, using a SPAtially Referenced Regression On Watershed attributes (SPARROW) model. We calibrated the model using TN and TP data from 26 and 27 sites, respectively. The results demonstrated that the R² values of TN and TP were both above 0.99. In 2013, the TN load delivered to Bohai Bay was 21,320 ton, which could be traced to various sources: upstream (39%), industrial discharge (10%), sewage discharge (34%), fertilizer application (3%), livestock breeding (7%), aquaculture (5%), and rural communities (2%). The TP load delivered to Bohai Bay was 1504 ton, which originated from upstream (33%), industrial discharge (5%), sewage discharge (21%), fertilizer application (5%), livestock breeding (12%), aquaculture (10%), and rural communities (14%). Rational management of the water resources in streams, enhancement of water circulation between rivers and wetlands, and making full use of the effect of both land and water on pollutant retention are the suitable strategies in watershed management, reducing marine pollution.

Using Multiobjective Optimization to Inform Green Infrastructure Decisions as Part of Robust Integrated Water Resources Management Plans

Uncertainty in the impacts of climate change and development on freshwater resources pose significant challenges for water resources management. Integrated and adaptive approaches to water resources management are a promising means of addressing uncertainty that afford flexibility in balancing multiple stakeholder objectives. However, guidance on designing such plans is lacking. In this study, we use multi-objective optimization to strategically incorporate green infrastructure (GI) into water resources management plans that maximize reductions in nutrient loads, minimize stormwater runoff, and minimize costs. Robust decision-making methods are applied to the resulting plan options to evaluate how optimized GI implementation varies under different possible future climates and to determine which solutions would be robust under a range of plausible future conditions. We demonstrate these coupled methods using a case study in southern Massachusetts, to address water quality issues related to point and nonpoint source nutrients in a rapidly developing watershed.