Watershed export of fine sediment, organic carbon, and chlorophyll-a to Chesapeake Bay: Spatial and temporal patterns in 1984-2016

Optical water typing in optically complex waters: A case study of Chesapeake Bay

Optical water typing has been widely used in aquatic research to classify water bodies based on their inherent optical properties as perceived through satellite-based measures of water color. While optical water type (OWT) classifications have primarily been used to better understand water color dynamics and improve satellite-based estimates of water clarity, chlorophyll a, and other optically active constituents, its potential for broader water quality assessment has received less attention. In this study, we examine the relationships between a suite of water quality parameters, including nutrient concentrations, and OWTs in Chesapeake Bay, an optically complex temperate estuary with an extensive water quality monitoring program. Using machine learning, we grouped Rrs data into ten dominant OWTs; the optimum number of clusters identified from a statistical within-cluster dispersion test. These OWTs ranged from brown to blue/green estuarine waters and emerged with high spatial contiguity. By analyzing synchronously measured discrete water quality variables grouped by corresponding OWTs, unexpected patterns became evident. Notably, total nitrogen concentration emerged as having statistically significant differences between OWTs, suggesting our approach can enhance understanding of nutrient pollution at the scale of a large optically complex estuary, especially in times of reduced fixed sampling routines (e.g., winter). This study aids in the interpretation of Bay-wide water quality trends, can assist in the dynamic selection of water quality retrieval algorithms, and provides high resolution data to identify regions of water quality impairment.

Developing a real-time water quality simulation toolbox using machine learning and application programming interface

Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreational spaces. However, most rivers are being increasingly contaminated by pollutants from non-point sources, urbanization, and other sources. Consequently, real-time river water quality modeling is essential for managing and protecting rivers from contamination, and its significance is growing across various sectors, including public health, agriculture, and water treatment systems. Therefore, a real-time river water quality simulation toolbox was developed using machine learning (ML) and an application program interface (API). To create the toolbox, models that simulated water quality parameters such as chlorophyll a (Chl-a), dissolved oxygen (DO), total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) at each point in the Nakdong River were constructed. The models were constructed using Artificial neural network (ANN), Random Forest (RF), support vector machines (SVM), and data from API. Subsequently, hyperparameter optimization was conducted to enhance the model's performance. During training, the models' performances were evaluated and compared based on the data sampling method and ML algorithms. Models trained with random sampling data outperformed those trained with time-series data. Among the algorithm models that used random sampling data, the RF exhibited the best performance. The average coefficient of determination (R2) values for each water quality simulation with randomly sampled data using RF for DO, TN, TP, Chl-a, and TOC were 0.79, 0.65, 0.74, 0.45, and 0.48, respectively. For ANN, they were 0.7, 0.51, 0.64, 0.35, and 0.35, respectively, and for SVM, they were 0.73, 0.51, 0.59, 0.21, and 0.3, respectively. The Chl-a and TOC models exhibited relatively poor performance, whereas the DO, TN, and TP models demonstrated superior performance. Diversifying the input data variables is necessary to improve the performance of the Chl-a and TOC models. Sensitivity and uncertainty analyses were conducted to evaluate and enhance the models' understanding. Furthermore, using a graphic user interface (GUI) toolbox, user convenience was maximized.

Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation

Many agricultural watersheds rely on the voluntary use of management practices (MPs) to reduce nonpoint source nutrient and sediment loads; however, the water-quality effects of MPs are uncertain. We interpreted water-quality responses from as early as 1985 through 2020 in three agricultural Chesapeake Bay watersheds that were prioritized for MP implementation, namely, the Smith Creek (Virginia), Upper Chester River (Maryland), and Conewago Creek (Pennsylvania) watersheds. We synthesized patterns in MPs, climate, land use, and nutrient inputs to better understand factors affecting nutrient and sediment loads. Relations between MPs and expected water-quality improvements were not consistently identifiable. The number of MPs increased in all watersheds since the early 2010s, but most monitored nutrient and sediment loads did not decrease. Nutrient and sediment loads increased or remained stable in Smith Creek and the Upper Chester River. Sediment loads and some nutrient loads decreased in Conewago Creek. In Smith Creek, a 36-year time-series model suggests that changes in manure affected flow-normalized total nitrogen loads. We hypothesize that increases in nutrient applications may overshadow some expected MP effects. MPs might have stemmed further water-quality degradation, but improvements in nutrient loads may rely on reducing manure and fertilizer applications. Our results highlight the importance of assessing MP performance with long-term monitoring-based studies.

Microbial diversity and abundance vary along salinity, oxygen, and particle size gradients in the Chesapeake Bay

Marine snow and other particles are abundant in estuaries, where they drive biogeochemical transformations and elemental transport. Particles range in size, thereby providing a corresponding gradient of habitats for marine microorganisms. We used standard normalized amplicon sequencing, verified with microscopy, to characterize taxon-specific microbial abundances, (cells per litre of water and per milligrams of particles), across six particle size classes, ranging from 0.2 to 500 μm, along the main stem of the Chesapeake Bay estuary. Microbial communities varied in salinity, oxygen concentrations, and particle size. Many taxonomic groups were most densely packed on large particles (in cells/mg particles), yet were primarily associated with the smallest particle size class, because small particles made up a substantially larger portion of total particle mass. However, organisms potentially involved in methanotrophy, nitrite oxidation, and sulphate reduction were found primarily on intermediately sized (5-180 μm) particles, where species richness was also highest. All abundant ostensibly free-living organisms, including SAR11 and Synecococcus, appeared on particles, albeit at lower abundance than in the free-living fraction, suggesting that aggregation processes may incorporate them into particles. Our approach opens the door to a more quantitative understanding of the microscale and macroscale biogeography of marine microorganisms.

Effects of point and nonpoint source controls on total phosphorus load trends across the Chesapeake Bay watershed, USA

Reduction of total phosphorus (TP) loads has long been a management focus of Chesapeake Bay restoration, but riverine monitoring stations have shown mixed temporal trends. To better understand the regional patterns and drivers of TP trends across the Bay watershed, we compiled and analyzed TP load data from 90 non-tidal network stations using clustering and random forest (RF) approaches. These stations were categorized into two distinct clusters of short-term (2013-2020) TP load trends, i.e. monotonic increase (n = 35) and monotonic decline (n = 55). RF models were developed to identify likely regional drivers of TP trend clusters. Reductions in point sources and agricultural nonpoint sources (i.e. fertilizer) both contributed to water-quality improvement in our period of analysis, thereby demonstrating the effectiveness of nutrient management and the importance of continuing such efforts. In addition, declining TP trends have a larger chance to occur in carbonate areas but a smaller chance in Coastal Plain areas, with the latter likely reflecting the effect of legacy P. To provide spatially explicit information, TP trend clusters were predicted for the entire watershed at the scale of river segments, which are more directly relevant to watershed planning. Among the 975 river segments, 544 (56%) and 431 (44%) were classified as 'monotonic increase' and 'monotonic decrease', respectively. Furthermore, these predicted TP trend clusters were paired with our previously published total nitrogen (TN) trend clusters, showing that TP and TN both declined in 185 segments (19%) and neither declined in 337 segments (35%). Broadly speaking, large-scale nutrient reduction efforts are underway in many regions to curb eutrophication. Water-quality responses and drivers may differ among systems, but our work provides important new evidence on the effectiveness of management efforts toward controlling point and nonpoint sources.

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.

Unravelling nutrient fate and CO2 concentrations in the reservoirs of the Seine Basin using a modelling approach

Reservoirs are active reactors for the biogeochemical cycling of carbon (C) and nutrients (nitrogen: N, phosphorus: P, and silica: Si), however, our in-depth understanding of C and nutrient cycling in reservoirs is still limited by the fact that it involves a variety of closely linked and coupled biogeochemical and hydrological processes. In this study, the updated process-based Barman model was applied to three reservoirs of the Seine Basin during 2019-2020, considering the variations of carbon dioxide (CO₂) concentrations and key water quality variables. The model simulations captured well the observed seasonal variations of water quality variables, although discrepancies remained for some variables. According to the model, we found that: (1) the three reservoirs are autotrophic ecosystems and showed high removal efficiency of dissolved inorganic carbon and nutrients during 2019-2020; (2) phytoplankton assimilation, benthic denitrification, precipitation and dissolution of calcium carbonate, and gas exchange at the water-air interface are the dominant processes for water quality variations in reservoirs; (3) based on scenarios results, trophic state and mean water depth of reservoir would impact the biogeochemical processes and the retention efficiency of nitrate and dissolved silicate. Finally, we expect that the successful application of Barman model in the reservoirs of the Seine Basin could provide a useful tool for simulating reservoir water quality changes and thus evaluating the impacts of reservoirs on downstream water quality.

Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications

Anthropogenic nutrient inputs have led to nutrient enrichment in many waterbodies worldwide, including Chesapeake Bay (USA). River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network that machine learning approaches - i.e., hierarchical clustering and random forest (RF) classification - can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks, resulting in information useful for watershed management. Cluster analysis revealed regional patterns of short-term TN trends (2007-2018) and categorized the stations into three distinct trend clusters, namely, V-shape (n = 23), monotonic decline (n = 35), and monotonic increase (n = 26). RF models identified regional drivers of TN trend clusters by quantifying the effects of watershed characteristics (land use, geology, physiography) and major N sources on the trend clusters. Results provide encouraging evidence that improved agricultural nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water-quality improvement in our period of analysis. Moreover, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in Coastal Plain watersheds, reflecting the effect of legacy N in groundwater. Notably, results show degrading trends in forested watersheds, suggesting new and/or remobilized sources that may compromise management efforts. Finally, the developed RF models were used to predict TN trend clusters for the entire Chesapeake Bay watershed at the fine scale of river segments (n = 979), providing fine spatial information that can facilitate targeted watershed management, including unmonitored areas. More broadly, this combined use of clustering and classification approaches can be applied to other regional monitoring networks to address similar water-quality questions.

A Comparison of Stream Water and Shallow Groundwater Suspended Sediment Concentrations in a West Virginia Mixed-Use, Agro-Forested Watershed

Suspended sediment is an important constituent of freshwater ecosystems that supports biogeochemical, geomorphological, and ecological processes. Current knowledge of suspended sediment is largely based on surface water studies; however, improved understanding of surface and in situ groundwater suspended sediment processes will improve pollutant loading estimates and watershed remediation strategies. A study was conducted in a representative mixed-use, agro-forested catchment of the Chesapeake Bay Watershed of the northeast, USA, utilizing an experimental watershed study design, including eight nested sub-catchments. Stream water and shallow groundwater grab samples were collected monthly from January 2020 to December 2020 (n = 192). Water samples were analyzed for suspended sediment using gravimetric (mg/L) and laser particle diffraction (µm) analytical methods. Results showed that shallow groundwater contained significantly higher (p < 0.001) total suspended solid concentrations and smaller particle sizes, relative to stream water. Differences were attributed to variability between sites in terms of soil composition, land use/land cover, and surficial geology, and also the shallow groundwater sampling method used. Results hold important implications for pollutant transport estimates and biogeochemical modeling in agro-forested watersheds. Continued work is needed to improve shallow groundwater suspended sediment characterization (i.e., mass and particle sizes) and the utility of this information for strategies that are designed to meet water quality goals.

Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data

Improving the spatiotemporal coverage of remote sensing (RS) products, such as sea surface chlorophyll concentration (SSCC), can offer a better understanding of the spatiotemporal SSCC distribution for ocean management purposes. In the first part of this work, 834 in-situ SSCC measurements of the SeaBASS-NASA (National Aeronautics and Space Administration) during 2002-2016 served as the empirical dataset. A moving window with ±3 days and ±0.5° centered at each of the in-situ SSCC measurements established a search neighborhood for Moderate Resolution Imaging Spectroradiometer Level 2 (MODIS L2) SSCC and MODIS L2 sea surface temperature (SST) data, and the matched SSCC and SST data were used for building a linear SSCC-SST relationship. The unmatched SST was introduced to the linear model for generating soft SSCC data with uniform distributions. The inherent spatiotemporal dependency of the SSCC distribution was then represented by the Bayesian maximum entropy (BME) method, which incorporated the soft SSCC data as auxiliary variable for SSCC estimation and mapping purposes. The results showed that a 75.3% accuracy improvement of remote SSCC retrieval in terms of R² can be achieved by BME-based method compared to the original MODIS L2 product. Subsequently, the BME-based method was applied to obtain daily SSCC dataset in Chesapeake Bay (USA) during the period 2010-2019. It was found that the SSCC distribution exhibited a decreasing spatial trend from the upper bay to the outer bay, whereas decreasing and increasing temporal trends were detected during the periods 2011-2014 and 2016-2019, respectively. The generalized Cauchy process was used to quantitatively describe the autocorrelation SSCC function in the Chesapeake Bay. The results showed that the outer bay exhibited the strongest long-range dependence among the four sub-regions, whereas the middle bay exhibited the weakest long-range dependence. Finally, one-point and two-point stochastic site indicators (SSIs) were employed to explore the spatiotemporal SSCC characteristics in Chesapeake Bay. The one-point SSI results showed that nearly 100% of the upper, middle and the lower bay areas experienced a high SSCC level (>5 mg/m³) during the entire study period. The area with SSCC >5 mg/m³ in the outer bay increased a lot during the winter season, but the area with SSCC >10 or 20 mg/m³ decreased significantly in the upper, middle and lower bay. Simultaneously, the SSCC dispersion in these areas was rather small during the winter season. On the other hand, the two-point SSI results showed that although the SSCC levels differ among the four sub-regions, but the SSCC connectivity structures between pairs of points also displayed some similarities in terms of their spatiotemporal dependency. In conclusion, the proposed BME-based method was shown to be a promising remote SSCC mapping technique that exhibited a powerful ability to improve both accuracy and coverage of RS products. The SSIs can be also used to explore the spatiotemporal characteristics of a variety of natural attributes in waters.

A multi-model approach to assessing the impacts of catchment characteristics on spatial water quality in the Great Barrier Reef catchments

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NO_X, NH₄ and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

Extreme Weather Events Enhance DOC Consumption in a Subtropical Freshwater Ecosystem: A Multiple-Typhoon Analysis

Empirical evidence suggests that the frequency/intensity of extreme weather events might increase in a warming climate. It remains unclear how these events quantitatively impact dissolved organic carbon (DOC), a pool approximately equal to CO₂ in the atmosphere. This study conducted a weekly-to-biweekly sampling in a deep subtropical reservoir in the typhoon-prevailing season (June to September) from 2004 to 2009, at which 33 typhoons with distinctive precipitation (<1~362 mm d^-1) had passed the study site. Our analyses indicated that the phosphate (i.e., DIP; <10~181 nMP) varied positively with the intensity of the accumulated rainfall 2-weeks prior; bacteria growth rate (0.05~3.68 d^-1) behaved as a positive function of DIP, and DOC concentrations (54~119 µMC) changed negatively with bacterial production (1.2~26.1 mgC m^-3 d^-1). These implied that the elevated DIP-loading in the hyperpycnal flow induced by typhoons could fuel bacteria growth and cause a significant decline of DOC concentrations. As the typhoon's intensity increases, many mineral-limited lentic freshwater ecosystems might become more like a CO₂ source injecting more CO₂ back to the atmosphere, creating a positive feedback loop that might generate severer extreme weather events.

An approach for decomposing river water-quality trends into different flow classes

A number of statistical approaches have been developed to quantify the overall trend in river water quality, but most approaches are not intended for reporting separate trends for different flow conditions. We propose an approach called FN_2Q, which is an extension of the flow-normalization (FN) procedure of the well-established WRTDS ("Weighted Regressions on Time, Discharge, and Season") method. The FN_2Q approach provides a daily time series of low-flow and high-flow FN flux estimates that represent the lower and upper half of daily riverflow observations that occurred on each calendar day across the period of record. These daily estimates can be summarized into any time period of interest (e.g., monthly, seasonal, or annual) for quantifying trends. The proposed approach is illustrated with an application to a record of total nitrogen concentration (632 samples) collected between 1985 and 2018 from the South Fork Shenandoah River at Front Royal, Virginia (USA). Results show that the overall FN flux of total nitrogen has declined in the period of 1985-2018, which is mainly attributable to FN flux decline in the low-flow class. Furthermore, the decline in the low-flow class was highly correlated with wastewater effluent loads, indicating that the upgrades of treatment technology at wastewater treatment facilities have likely led to water-quality improvement under low-flow conditions. The high-flow FN flux showed a spike around 2007, which was likely caused by increased delivery of particulate nitrogen associated with sediment transport. The case study demonstrates the utility of the FN_2Q approach toward not only characterizing the changes in river water quality but also guiding the direction of additional analysis for capturing the underlying drivers. The FN_2Q approach (and the published code) can easily be applied to widely available river monitoring records to quantify water-quality trends under different flow conditions to enhance understanding of river water-quality dynamics.

Disturbance mechanisms of lacustrine organic carbon burial: Case study of Cuopu Lake, Southwest China

Lakes are important organic carbon (OC) traps in the global carbon cycle. Recent studies have shown that the rate of OC burial in lacustrine sediments is influenced by factors such as climate change, land-use change, and eutrophication. In this study, we use multiproxy methods to reveal the mechanisms of lacustrine sediment OC burial in an alpine lake (Cuopu Lake), in southwest China. Combined with the dating from ²¹⁰Pb_ex and n-alkanes distribution analysis using the Positive Matrix Factorization model, the sedimentary history was divided into five stages: religious activity (the 1840s-1880s), earthquake (the 1880s-1910s), garrison (the 1910s-1960s), transition (the 1960s-1990s), and ecotourism (the 1990s-2010s). During the earthquake stage, OC burial was dominated by terrestrial solids (>40%) and co-precipitated algae (>30%), with a rapid deposition rate (>4 mm a^-1) and low OC concentration (<4 mg g^-1). During the other stages, when the level of disturbance was relatively low, a change in nutrient conditions either promoted or inhibited plant growth, which influenced the type of buried OC. The contribution of OC derived from combustion sources varied from stage to stage. Severe anthropogenic disturbances have led to a significant increase in nutritional levels in the lake water, leading to an increase in the OC burial rate. Climate change, which leads to changes in temperature and rainfall, did not significantly influence OC burial, whereas nitrogen deposition (and associated ecological changes) was a significant determinant. When the general mechanism is dominant, the total nitrogen to inorganic phosphorus ratio is an effective indicator of OC burial due to its selective promotion of different plant types. In conclusion, our results suggest that lacustrine sediment OC burial is closely linked to physical and anthropogenic factors in Cuopu Lake, as well as similar montane lakes.

A simulation-based approach to assess the power of trend detection in high- and low-frequency water quality records

To provide more precise understanding of water quality changes, continuous sampling is being used more in surface water quality monitoring networks. However, it remains unclear how much improvement continuous monitoring provides over spot sampling, in identifying water quality changes over time. This study aims (1) to assess our ability to detect trends using water quality data of both high and low frequencies and (2) to assess the value of using high-frequency data as a surrogate to help detect trends in other constituents. Statistical regression models were used to identify temporal trends and then to assess the trend detection power of high-frequency (15 min) and low-frequency (monthly) data for turbidity and electrical conductivity (EC) data collected across Victoria, Australia. In addition, we developed surrogate models to simulate five sediment and nutrients constituents from runoff, turbidity and EC. A simulation-based statistical approach was then used to the compare the power to detect trends between the low- and high-frequency water quality records. Results show that high-frequency sampling shows clear benefits in trend detection power for turbidity, EC, as well as simulated sediment and nutrients, especially over short data periods. For detecting a 1% annual trend with 5 years of data, up to 97% and 94% improvements on the trend detection probability are offered by high-frequency data compared with monthly data, for turbidity and EC, respectively. Our results highlight the benefits of upgrading monitoring networks with wider application of high-frequency sampling.

A Comparison and Validation of Saturated Hydraulic Conductivity Models

Saturated hydraulic conductivity (Ksat) is fundamental to shallow groundwater processes. There is an ongoing need for observed and model validated Ksat values. A study was initiated in a representative catchment of the Chesapeake Bay Watershed in the Northeast USA, to collect observed Ksat and validate five Ksat pedotransfer functions. Soil physical characteristics were quantified for dry bulk density (bdry), porosity, and soil texture, while Ksat was quantified using piezometric slug tests. Average bdry and porosity ranged from 1.03 to 1.30 g/cm3 and 0.51 to 0.61, respectively. Surface soil (0–5 cm) bdry and porosity were significantly (p < 0.05) lower and higher, respectively, than deeper soils (i.e., 25–30 cm; 45–50 cm). bdry and porosity were significantly different with location (p < 0.05). Average soil composition was 92% sand. Average Ksat ranged from 0.29 to 4.76 m/day and significantly differed (p < 0.05) by location. Four models showed that spatial variability in farm-scale Ksat estimates was small (CV < 0.5) and one model performed better when Ksat was 1.5 to 2.5 m/day. The two-parameter model that relied on silt/clay fractions performed best (ME = 0.78 m/day; SSE = 20.68 m2/day2; RMSE = 1.36 m/day). Results validate the use of simple, soil-property-based models to predict Ksat, thereby increasing model applicability and transferability.

Temporal variation in riverine organic carbon concentrations and fluxes in two contrasting estuary systems: Geum and Seomjin, South Korea

In this study, surface water samples were collected at sites located in the lowest reaches of closed (Geum) (i.e. with an estuary dam at the river mouth) and open (Seomjin) estuary systems between May 2016 and May 2018. We analyzed concentrations and stable isotopes of particulate organic carbon (POC) and dissolved organic carbon (DOC) to assess OC sources, to estimate fluxes of riverine OC, and to assess some of the factors driving OC exports in these two contrasting Korean estuary systems. Our geochemical results suggest that the contribution of the phytoplankton-derived POC to the total POC pool was larger in the Geum River than in the Seomjin River. Notably, a heavy riverine algae bloom occurred in the Geum River in August 2016, resulting in a high carbon isotopic composition (-19.4‰) together with low POC/PN ratio (<10) and POC/Chl-a ratio (<100). In contrast, potential DOC sources in both the Geum River and the Seomjin River were a mixture of C₃-derived forest soils and cropland organic matter. During the study period, the catchment area-normalized fluxes of POC and DOC were 0.40 × 10^-3 tC/km²/yr and 6.5 × 10^-2 tC/km²/yr in the Geum River and 5.2 × 10^-4 tC/km²/yr and 8.6 × 10^-4 tC/km²/yr in the Seomjin River, respectively. It appears that the POC flux was more weakly associated with the water discharge in the Geum River than in the Seomjin River, but the DOC fluxes were in general controlled by the water discharges in both rivers. Accordingly, the estuary dam of the Geum River might be one of the most strongly influencing factors on seasonal patterns in POC fluxes into the adjacent coastal seas, strongly modifying water residence times and thus biogeochemical processes.

Dramatic hydrodynamic and sedimentary responses in Galveston Bay and adjacent inner shelf to Hurricane Harvey

Hurricane Harvey, one of the worst hurricanes that hit the United States in recent history, poured record-breaking rainfall across the Houston metropolitan area. Based on a comprehensive set of data from various sources, we examined the dramatic responses in hydrodynamic and sedimentary processes of Galveston Bay to this extreme event. Using a freshwater fraction method that circumvents the uncertainties in surface runoff and groundwater discharge, the freshwater load into the bay during Harvey and the following month was estimated to be 11.1 × 10⁹ m³, about 3 times the bay volume, which had completely refreshed the entire bay. Harvey also delivered 9.86 × 10⁷ metric tons of sediment into the bay, equivalent to 18 years of average annual sediment load. At a site inside the San Jacinto Estuary, acute bed erosion of 48 cm followed by deposition of 22 cm of new sediment was observed from the sediment cores. Slow salinity recovery (~2 month) and a thick flood deposit (~10.5 cm average over the entire bay) had likely impacted the ecosystem in the bay and the adjacent inner shelf. Estuaries with similar bathymetric and geometric characteristics, i.e., shallow bathymetry with narrow outlets, are expected to experience similar dramatic estuarine responses while extreme precipitation events are expected to occur more frequently under the warming climate.

Chesapeake Bay's water quality condition has been recovering: Insights from a multimetric indicator assessment of thirty years of tidal monitoring data

To protect the aquatic living resources of Chesapeake Bay, the Chesapeake Bay Program partnership has developed guidance for state water quality standards, which include ambient water quality criteria to protect designated uses (DUs), and associated assessment procedures for dissolved oxygen (DO), water clarity/underwater bay grasses, and chlorophyll-a. For measuring progress toward meeting the respective states' water quality standards, a multimetric attainment indicator approach was developed to estimate combined standards attainment. We applied this approach to three decades of monitoring data of DO, water clarity/underwater bay grasses, and chlorophyll-a data on annually updated moving 3-year periods to track the progress in all 92 management segments of tidal waters in Chesapeake Bay. In 2014-2016, 40% of tidal water segment-DU-criterion combinations in the Bay (n = 291) are estimated to meet thresholds for attainment of their water quality criteria. This index score marks the best 3-year status in the entire record. Since 1985-1987, the indicator has followed a nonlinear trajectory, consistent with impacts from extreme weather events and subsequent recoveries. Over the period of record (1985-2016), the indicator exhibited a positive and statistically significant trend (p < 0.05), indicating that the Bay has been recovering since 1985. Patterns of attainment of individual DUs are variable, but improvements in open water DO, deep channel DO, and water clarity/submerged aquatic vegetation have combined to drive the improvement in the Baywide indicator in 2014-2016 relative to its long-term median. Finally, the improvement in estimated Baywide attainment was statistically linked to the decline of total nitrogen, indicating responsiveness of attainment status to the reduction of nutrient load through various management actions since at least the 1980s.

A simple approach to estimate daily loads of total, refractory, and labile organic carbon from their seasonal loads in a watershed

Loads of naturally occurring total organic carbons (TOC), refractory organic carbon (ROC), and labile organic carbon (LOC) in streams control the availability of nutrients and the solubility and toxicity of contaminants and affect biological activities through absorption of light and complex metals with production of carcinogenic compounds. Although computer models have become increasingly popular in understanding and management of TOC, ROC, and LOC loads in streams, the usefulness of these models hinges on the availability of daily data for model calibration and validation. Unfortunately, these daily data are usually insufficient and/or unavailable for most watersheds due to a variety of reasons, such as budget and time constraints. A simple approach was developed here to calculate daily loads of TOC, ROC, and LOC in streams based on their seasonal loads. We concluded that the predictions from our approach adequately match field measurements based on statistical comparisons between model calculations and field measurements. Our approach demonstrates that an increase in stream discharge results in increased stream TOC, ROC, and LOC concentrations and loads, although high peak discharge did not necessarily result in high peaks of TOC, ROC, and LOC concentrations and loads. The approach developed herein is a useful tool to convert seasonal loads of TOC, ROC, and LOC into daily loads in the absence of measured daily load data.

Chesapeake Bay Dissolved Oxygen Criterion Attainment Deficit: Three Decades of Temporal and Spatial Patterns

Low dissolved oxygen (DO) conditions are a recurring issue in waters of Chesapeake Bay, with detrimental effects on aquatic living resources. The Chesapeake Bay Program partnership has developed criteria guidance supporting the definition of state water quality standards and associated assessment procedures for DO and other parameters, which provides a binary classification of attainment or impairment. Evaluating time series of these two outcomes alone, however, provides limited information on water quality change over time or space. Here we introduce an extension of the existing Chesapeake Bay water quality criterion assessment framework to quantify the amount of impairment shown by space-time exceedance of DO criterion ("attainment deficit") for a specific tidal management unit (i.e., segment). We demonstrate the usefulness of this extended framework by applying it to Bay segments for each 3-year assessment period between 1985 and 2016. In general, the attainment deficit for the most recent period assessed (i.e., 2014-2016) is considerably worse for deep channel (DC; n = 10) segments than open water (OW; n = 92) and deep water (DW; n = 18) segments. Most subgroups - classified by designated uses, salinity zones, or tidal systems - show better (or similar) attainment status in 2014-2016 than their initial status (1985-1987). Some significant temporal trends (p < 0.1) were detected, presenting evidence on the recovery for portions of Chesapeake Bay with respect to DO criterion attainment. Significant, improving trends were observed in seven OW segments, four DW segments, and one DC segment over the 30 3-year assessment periods (1985-2016). Likewise, significant, improving trends were observed in 15 OW, five DW, and four DC segments over the recent 15 assessment periods (2000-2016). Subgroups showed mixed trends, with the Patuxent, Nanticoke, and Choptank Rivers experiencing significant, improving short-term (2000-2016) trends while Elizabeth experiencing a significant, degrading short-term trend. The general lack of significantly improving trends across the Bay suggests that further actions will be necessary to achieve full attainment of DO criterion. Insights revealed in this work are critical for understanding the dynamics of the Bay ecosystem and for further assessing the effectiveness of management initiatives aimed toward Bay restoration.