High-frequency Water Quality Data Research Articles

Examining contaminant transport hotspots and their predictability across contrasted watersheds.

Hydrobiogeochemical processes governing water quantity and quality are highly variable in space and time. Focusing on thirty river locations in Québec, Canada, three water quality hotness indices were used to classify watersheds as contaminant transport hotspots. Concentration and load data for suspended solids (SS), total nitrogen (TN), and total phosphorous (TP) were used to identify transport hotspots, and results were compared across hotness indices with different data requirements. The role of hydroclimatic and physiographic characteristics on the occurrence and temporal persistence of transport hotspots was examined. Results show that the identification of transport hotspots was dependent on both the type of data and the hotness index used. Relationships between temporal and spatial predictors, however, were generally consistent. Annual transport hotspot occurrence was found to be related to temporal characteristics such as the number of dry days, potential evapotranspiration, and snow water equivalent, while hotspot temporal persistence was correlated to landcover characteristics. Stark differences in the identification of SS, TN, and TP transport hotspots were attributed to differences in mobilization processes and provided insights into dominant water and nutrient flowpaths in the studied watersheds. This study highlighted the importance of comparing contaminant dynamics across watersheds even when high-frequency water quality data or discharge data are not available. Characterizing hotspot occurrence and persistence, among hotness indices and water quality parameters, could be useful for watershed managers when identifying problematic watersheds, exploring legacy effects, and establishing a prioritization framework for areas that would benefit from enhanced routine monitoring or targeted mitigation strategies.

Improved data assimilation for algal bloom dynamics simulation in the Three Gorges Reservoir using particle filter

Algal blooms have been increasingly prevalent in recent years, especially in lakes and reservoirs; their accurate prediction is essential for preserving water quality. In this study, the observed chlorophyll a (chl-a) levels were assimilated into the Environmental Fluid Dynamics Code (EFDC) of algal bloom dynamics by using a particle filter (PF), and the state variables of water quality and model parameters were simultaneously updated to achieve enhanced algal bloom predictive performance. The developed data assimilation system for algal blooms was applied to Xiangxi Bay (XXB) in the Three Gorges Reservoir (TGR). The results show that the ensemble mean accuracy and reliability of the confidence intervals of the predicted state variables, including chl-a and indirectly updated phosphate (PO4), ammonium (NH4), and nitrate (NO3) levels, were considerably improved after PF assimilation. Thus, PF assimilation is an effective tool for the dynamic correction of parameters to represent their inherent variations. Increased assimilation frequency can effectively suppress the accumulation of model errors; therefore, the use of high-frequency water quality data for assimilation is recommended to ensure more accurate and reliable algal bloom prediction.

Three-dimensional augmentation for hyperspectral image data of water quality: An Integrated approach using machine learning and numerical models

This research introduces a comprehensive methodology to enhance hyperspectral image data (HSD) utility, specifically focusing on the three-dimensional (3-D) augmentation of Chlorophyll-a (Chl-a). This study comprises three significant steps: (1) the augmentation of limited field water quality data in terms of time interval and number of variables using neural network models, (2) the generation of 3-D data using numerical models, and (3) the extension of the hyperspectral image data into 3-D data using machine learning models. In the first phase, Multilayer Perceptron (MLP) models were developed to train water quality interactions and successfully generated high-frequency water quality data by adjusting biased measurements and predicting detailed water quality variables. In the second phase, high-frequency data generated by MLP models were applied to develop two numerical models. These numerical models successfully generated 3-D data, thereby demonstrating the effectiveness of integrating numerical modeling with neural networks. In the final phase, ten machine learning models were trained to generate 3-D Chl-a data from HSD. Notably, the Gaussian Process Regression model exhibited superior performance, effectively estimating 3-D Chl-a data with robust accuracy, as evidenced by an R-square value of 0.99. The findings align with theories of algal bloom dynamics, further validating the effectiveness of the approach. This study demonstrated the successful integrated development for HSD extension using machine learning models, numerical models, and original HSD, highlighting the potential of such integrated methodologies in advancing water quality monitoring and estimation. Notably, the approach leverages readily accessible data, allowing for the swift generation of results and bypassing time-consuming data collection processes. This research marks a significant step towards more robust, comprehensive water quality monitoring and prediction, thereby facilitating better management of aquatic ecosystems.

Chlorophyll a Predictions in a Piedmont Lake in Upstate South Carolina Using Machine-Learning Approaches

Freshwater systems are often breeding grounds for harmful algal blooms (HABs), although they are more dominant in ponds and lakes due to the prevailing conditions in those bodies of water. Therefore, the monitoring, modeling, and management of HABs requires knowledge of the complex interrelationship between factors that influence HABs and their detrimental effect on the ecosystem. High concentrations of chlorophyll a are often used to measure algal blooms in bodies of water. Generally, water samples are collected from the field and the concentration of chlorophyll a is measured in a laboratory and compared to water quality standards in order to indicate the potential presence or absence of an algal bloom. While numerical water quality models can help answer some of the critical environmental conditions that affect HABs and their effective management, numerous model inputs, the uncertainty in model predictions, and the complexity of HABs ecosystems encourage the application of newly rising data-driven models. The current study utilized high-frequency water quality data and investigated machine-learning algorithms (random forest (RF) and artificial neural network (ANN)) to predict chlorophyll a concentrations in Boyd Millpond, a lake in Upstate South Carolina. The model performances were compared using root mean square error (RMSE), coefficient of determination (R2), and correlation coefficient. The water quality parameters used as inputs were pH, specific conductivity, dissolved oxygen, saturated dissolved oxygen, temperature, oxidation-reduction potential (ORP), and turbidity, while chlorophyll a was selected as the target variable. The results from this study showed that RF performed better than ANN. The error metrics observed using all parameters as input were RMSE, R2, and correlation with values 0.00013, 0.86, and 0.93, respectively, when testing the RF model and 0.00025, 0.74, and 0.86, respectively, during the testing stage of the ANN model. The Least Absolute Shrinkage and Selection Operator (LASSO) was used for variable selection and identified pH and specific conductivity as essential parameters. The broader outcome of this research upon further field validation will enable the timely detection of HABs with chlorophyll a as a signal to instigate further tests and early warning for recreational activities and livestock protection and initiate countermeasures to safeguard the lives of aquatic organisms.

Large-stream nitrate retention patterns shift during droughts: Seasonal to sub-daily insights from high-frequency data-model fusion.

High-frequency nitrate-N (NO3--N) data are increasingly available, while accurate assessments of in-stream NO3--N retention in large streams and rivers require a better capture of complex river hydrodynamic conditions. This study demonstrates a fusion framework between high-frequency water quality data and hydrological transport models, that (1) captures river hydraulics and their impacts on solute signal propagation through river hydrodynamic modeling, and (2) infers in-stream retention as the differences between conservatively traced and reactively observed NO3--N signals. Using this framework, continuous 15-min estimates of NO3--N retention were derived in a 6th-order reach of the lower Bode River (27.4km, central Germany), using long-term sensor monitoring data during a period of normal flow from 2015 to 2017 and a period of drought from 2018 to 2020. The unique NO3--N retention estimates, together with metabolic characteristics, revealed insightful seasonal patterns (from high net autotrophic removal in late-spring to lower rates, to net heterotrophic release during autumn) and drought-induced variations of those patterns (reduced levels of net removal and autotrophic nitrate removal largely buffered by heterotrophic release processes, including organic matter mineralization). Four clusters of diel removal patterns were identified, potentially representing changes in dominant NO3--N retention processes according to seasonal and hydrological conditions. For example, dominance of autotrophic NO3--N retention extended more widely across seasons during the drought years. Such cross-scale patterns and changes under droughts are likely co-determined by catchment and river environments (e.g., river primary production, dissolved organic carbon availability and its quality), which resulted in more complex responses to the sequential droughts. Inferences derived from this novel data-model fusion provide new insights into NO3- dynamics and ecosystem function of large streams, as well as their responses to climate variability. Moreover, this framework can be flexibly transferred across sites and scales, thereby complementing high-frequency monitoring to identify in-stream retention processes and to inform river management.

Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation

High-frequency monitoring of water quality in catchments brings along the challenge of post-processing large amounts of data. Moreover, monitoring stations are often remote and technical issues resulting in data gaps are common. Machine learning algorithms can be applied to fill these gaps, and to a certain extent, for predictions and interpretation. The objectives of this study were (1) to evaluate six different machine learning models for gap-filling in a high-frequency nitrate and total phosphorus concentration time series, (2) to showcase the potential added value (and limitations) of machine learning to interpret underlying processes, and (3) to study the limits of machine learning algorithms for predictions outside the training period. We used a 4-year high-frequency dataset from a ditch draining one intensive dairy farm in the east of The Netherlands. Continuous time series of precipitation, evapotranspiration, groundwater levels, discharge, turbidity, and nitrate or total phosphorus were used as predictors for total phosphorus and nitrate concentrations respectively. Our results showed that the random forest algorithm had the best performance to fill in data-gaps, with R2 higher than 0.92 and short computation times. The feature importance helped understanding the changes in transport processes linked to water conservation measures and rain variability. Applying the machine learning model outside the training period resulted in a low performance, largely due to system changes (manure surplus and water conservation) which were not included as predictors. This study offers a valuable and novel example of how to use and interpret machine learning models for post-processing high-frequency water quality data.

Particle filter for high frequency oxygen data assimilation in river systems

High frequency water quality data measured by in-situ sensors allowed the development of auto-calibration methods for water quality simulation programs. However, these methods consider static parameter values, which may be unrealistic for microorganism activities. An alternative technique is to use data assimilation methods. This paper presents a first application of a particle filter that assimilates dissolved oxygen (DO) data into the hydro-ecological model ProSe. It demonstrates the capability of the approach for simulating DO concentrations and characterizing time-varying physiological properties of living communities in contrasted trophic contexts. DO concentrations are better estimated, especially during algal bloom when phytoplankton physiological parameters match the ones reported in the literature. Despite the simulation capabilities related to phytoplankton, further improvements related to low flow periods can still be achieved, especially concerning the heterotrophic bacteria properties as well as a finer description of the biodegradable component of the organic matter flux at the system's boundaries.

Nitrogen dynamics and nitrogen-to-phosphorus stoichiometry in cold region agricultural streams.

Cold agricultural regions are getting warmer and experiencing shifts in precipitation patterns, which affect hydrological transport of nutrients through reduced snowpack and higher annual proportions of summer rainfall. Previous work has demonstrated that the timing of phosphorus (P) concentrations is regionally coherent in streams of the northern Great Plains, suggesting a common climatic driver. There has been less investigation into patterns of stream nitrogen (N), despite its importance for water quality. Using high-frequency water quality data collected over 6 yr from three southern Manitoba agricultural streams, the goal of this research was to investigate seasonal patterns in N and P concentrations and the resultant impacts of these patterns on N/P stoichiometry. In the spring, high concentrations of inorganic N were associated with snowmelt runoff, while summer N was dominated by organic forms; inorganic N concentrations remained consistently low in the summer, suggesting increased biological N transformation and N removal. Relationships between N concentration and discharge showed generally weak model fits (r2 values for significant relationships ranging from .33 to .48), and the strength and direction of model fits differed among streams, seasons, and forms of N. Dissolved organic N concentrations were strongly associated with dissolved organic carbon. Nitrogen-to-phosphorus ratios varied among streams but were significantly lower during summer storm events (p<.0001). These results suggest that climate-driven shifts in temperature and precipitation may negatively affect downstream water quality in this region.

Detecting Technical Anomalies in High-Frequency Water-Quality Data Using Artificial Neural Networks.

Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency of anomalous events, the broad-range of possible anomaly types, and local nonstationary environmental conditions, suggesting the need for flexible statistical methods that are able to cope with unbalanced high-volume data problems. Here, we aimed to detect anomalies caused by technical errors in water-quality (turbidity and conductivity) data collected by automated in situ sensors deployed in contrasting riverine and estuarine environments. We first applied a range of artificial neural networks that differed in both learning method and hyperparameter values, then calibrated models using a Bayesian multiobjective optimization procedure, and selected and evaluated the "best" model for each water-quality variable, environment, and anomaly type. We found that semi-supervised classification was better able to detect sudden spikes, sudden shifts, and small sudden spikes, whereas supervised classification had higher accuracy for predicting long-term anomalies associated with drifts and periods of otherwise unexplained high variability.

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors

Monitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.

Mixing data modelling method for response of water environment quality to economic development

Environmental Kuznets Curve (EKC) is one of the main tools to analyze the response relationship between water environmental quality and economic development. In classical EKC curve modeling process, the environmental index time series and economic index time series must be the same frequency. Because basin water-quality in China has been gradually monitored and released with high frequency, and most macroeconomic indicators are on annual basis, therefore, by the construction of three classes mix data polynomial equation of EKC curve, the mixing data modeling method of the response relationship between water quality and economic development is proposed to adapt to the situation. The mix data model not only avoids destroying original information due to simple low-frequency measurement of high frequency water quality data using classical model, but also through direct mining of high frequency water quality data, the lag time of water environment quality response to economic development can be obtained. To verify the validity of the model, according to the monitoring and statistical data of 2009-2017, a prefecture level city in Henan province of Haihe River Basin is taken as the research object, and EKC three classes mix data polynomial equation of chemical oxygen demand (COD) concentration and ammonia nitrogen concentration is established.

Water quality monitoring in smart city: A pilot project

Abstract A smart city is an urban development vision to integrate multiple information and communication technology (ICT), “Big Data” and Internet of Things (IoT) solutions in a secure fashion to manage a city's assets for sustainability, resilience and liveability. Meanwhile, water quality monitoring has been evolving to the latest wireless sensor network (WSN) based solutions in recent decades. This paper presents a multi-parameter water quality monitoring system of Bristol Floating Harbour which has successfully demonstrated the feasibility of collecting real-time high-frequency water quality data and displayed the real-time data online. The smart city infrastructure – Bristol Is Open was utilised to provide a plug & play platform for the monitoring system. This new system demonstrates how a future smart city can build the environment monitoring system benefited by the wireless network covering the urban area. The system can be further integrated in the urban water management system to achieve improved efficiency.

Identification of long-term trends and seasonality in high-frequency water quality data from the Yangtze River basin, China.

Comprehensive understanding of the long-term trends and seasonality of water quality is important for controlling water pollution. This study focuses on spatio-temporal distributions, long-term trends, and seasonality of water quality in the Yangtze River basin using a combination of the seasonal Mann-Kendall test and time-series decomposition. The used weekly water quality data were from 17 environmental stations for the period January 2004 to December 2015. Results show gradual improvement in water quality during this period in the Yangtze River basin and greater improvement in the Uppermost Yangtze River basin. The larger cities, with high GDP and population density, experienced relatively higher pollution levels due to discharge of industrial and household wastewater. There are higher pollution levels in Xiang and Gan River basins, as indicated by higher NH4-N and CODMn concentrations measured at the stations within these basins. Significant trends in water quality were identified for the 2004–2015 period. Operations of the three Gorges Reservoir (TGR) enhanced pH fluctuations and possibly attenuated CODMn, and NH4-N transportation. Finally, seasonal cycles of varying strength were detected for time-series of pollutants in river discharge. Seasonal patterns in pH indicate that maxima appear in winter, and minima in summer, with the opposite true for CODMn. Accurate understanding of long-term trends and seasonality are necessary goals of water quality monitoring system efforts and the analysis methods described here provide essential information for effectively controlling water pollution.

Identifying multiple stressor controls on phytoplankton dynamics in the River Thames (UK) using high-frequency water quality data

River phytoplankton blooms can pose a serious risk to water quality and the structure and function of aquatic ecosystems. Developing a greater understanding of the physical and chemical controls on the timing, magnitude and duration of blooms is essential for the effective management of phytoplankton development. Five years of weekly water quality monitoring data along the River Thames, southern England were combined with hourly chlorophyll concentration (a proxy for phytoplankton biomass), flow, temperature and daily sunlight data from the mid-Thames. Weekly chlorophyll data was of insufficient temporal resolution to identify the causes of short term variations in phytoplankton biomass. However, hourly chlorophyll data enabled identification of thresholds in water temperature (between 9 and 19°C) and flow (<30m3s−1) that explained the development of phytoplankton populations. Analysis showed that periods of high phytoplankton biomass and growth rate only occurred when these flow and temperature conditions were within these thresholds, and coincided with periods of long sunshine duration, indicating multiple stressor controls. Nutrient concentrations appeared to have no impact on the timing or magnitude of phytoplankton bloom development, but severe depletion of dissolved phosphorus and silicon during periods of high phytoplankton biomass may have contributed to some bloom collapses through nutrient limitation. This study indicates that for nutrient enriched rivers such as the Thames, manipulating residence time (through removing impoundments) and light/temperature (by increasing riparian tree shading) may offer more realistic solutions than reducing phosphorus concentrations for controlling excessive phytoplankton biomass.

Chemical Fouling Reduction of a Submersible Steel Spectrophotometer in Estuarine Environments Using a Sacrificial Zinc Anode

The availability of in situ spectrophotometers, such as the S::CAN spectro::lyser, has expanded the possibilities for high-frequency water quality data collection. However, biological and chemical fouling can degrade the performance of in situ spectrophotometers, especially in saline environments with rapid flow rates. A complex freshwater washing system has been previously designed to reduce chemical fouling for the S::CAN spectro::lyser spectrophotometer. In the current study, we present a simpler, cheaper alternative: the attachment of a sacrificial zinc anode. Results are presented detailing the S::CAN spectro::lyser performance with and without the addition of the sacrificial anode. Attachment of the zinc anode provided efficient corrosion protection during 2-wk deployments in a highly dynamic (average tidal range, 2.5 m) saline tidal saltmarsh creek at Groves Creek, Skidaway Institute of Oceanography, Savannah, GA.

Does river restoration affect diurnal and seasonal changes to surface water quality? A study along the Thur River, Switzerland

Changes in river water quality were investigated along the lower reach of the Thur River, Switzerland, following river restoration and a summer storm event. River restoration and hydrological storm events can each cause dramatic changes to water quality by affecting various bio-geochemical processes in the river, but have to date not been well documented, especially in combination. Evaluating the success of river restoration is often restricted in large catchments due to a lack of high frequency water quality data, which are needed for process understanding. These challenges were addressed in this study by measuring water quality parameters including dissolved oxygen (DO), temperature, pH, electrical conductivity (EC), nitrate and dissolved organic carbon (DOC) with a high temporal frequency (15min–1h) over selected time scales. In addition, the stable isotopes of water (δD and δ18O-H2O) as well as those of nitrate (δ15N-NO3− and δ18O-NO3−) were measured to follow changes in water quality in response to the hydrological changes in the river. To compare the spatial distribution of pre- and post-restoration water quality, the sampling stations were chosen upstream and downstream of the restored section. The diurnal and seasonal changes were monitored by conducting 24-hour campaigns in three seasons (winter, summer and autumn) in 2012 and 2013. The amplitude of the diurnal changes of the various observed parameters showed significant seasonal and spatial variability. Biological processes — mainly photosynthesis and respiration — were found to be the major drivers of these diurnal cycles. During low flow in autumn, a reduction of nitrate (attributed to assimilation by autotrophs) in the pre-dawn period and a production of DOC during the daytime (attributed to photosynthesis) were observed downstream of the restored site. Further, a summer storm event was found to override the influence of these biological processes that control the diurnal changes. High frequency daily monitoring of key water quality parameters over different seasons is shown to be essential in evaluating river restoration success.

Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive

Abstract. The EU Water Framework Directive (WFD) requires that the ecological and chemical status of water bodies in Europe should be assessed, and action taken where possible to ensure that at least "good" quality is attained in each case by 2015. This paper is concerned with the accuracy and precision with which chemical status in rivers can be measured given certain sampling strategies, and how this can be improved. High-frequency (hourly) chemical data from four rivers in southern England were subsampled to simulate different sampling strategies for four parameters used for WFD classification: dissolved phosphorus, dissolved oxygen, pH and water temperature. These data sub-sets were then used to calculate the WFD classification for each site. Monthly sampling was less precise than weekly sampling, but the effect on WFD classification depended on the closeness of the range of concentrations to the class boundaries. In some cases, monthly sampling for a year could result in the same water body being assigned to three or four of the WFD classes with 95% confidence, due to random sampling effects, whereas with weekly sampling this was one or two classes for the same cases. In the most extreme case, the same water body could have been assigned to any of the five WFD quality classes. Weekly sampling considerably reduces the uncertainties compared to monthly sampling. The width of the weekly sampled confidence intervals was about 33% that of the monthly for P species and pH, about 50% for dissolved oxygen, and about 67% for water temperature. For water temperature, which is assessed as the 98th percentile in the UK, monthly sampling biases the mean downwards by about 1 °C compared to the true value, due to problems of assessing high percentiles with limited data. Low-frequency measurements will generally be unsuitable for assessing standards expressed as high percentiles. Confining sampling to the working week compared to all 7 days made little difference, but a modest improvement in precision could be obtained by sampling at the same time of day within a 3 h time window, and this is recommended. For parameters with a strong diel variation, such as dissolved oxygen, the value obtained, and thus possibly the WFD classification, can depend markedly on when in the cycle the sample was taken. Specifying this in the sampling regime would be a straightforward way to improve precision, but there needs to be agreement about how best to characterise risk in different types of river. These results suggest that in some cases it will be difficult to assign accurate WFD chemical classes or to detect likely trends using current sampling regimes, even for these largely groundwater-fed rivers. A more critical approach to sampling is needed to ensure that management actions are appropriate and supported by data.

Using in situ ultraviolet‐visual spectroscopy to measure nitrogen, carbon, phosphorus, and suspended solids concentrations at a high frequency in a brackish tidal marsh

The collection of high frequency water quality data are key to making the next leap in hydrological and biogeochemical sciences. Commercially available in situ ultraviolet‐visual (UV‐Vis) spectrometers make possible the long‐term collection of absorption spectra multiple times per hour. This technology has proven useful for measuring nitrate, dissolved organic carbon, and total suspended solids in many environments, but has not been tested in tidal marsh conditions where upstream freshwater mixes with estuarine waters, resulting in rapid changes in concentrations and salinity. These three parameters encompass only a portion of the nutrients that are of interest in these systems. To test the potential of spectroscopy to measure these and other nutrient concentrations, spectrometers were installed in a constructed brackish tidal marsh and absorbance spectra were compared to lab analyses for coinciding discrete samples. Variable selection techniques, including partial least squares regression, lasso regression, and stepwise regression, were used to develop models with which nitrate, total kjeldahl nitrogen, dissolved organic carbon, phosphate, total phosphorus, total suspended solids, and salinity in brackish marsh waters can be predicted from UV‐Vis spectrometer measurements. Significant relationships between the absorption spectra and the laboratory measured concentrations were observed for all of the parameters. Phosphate and total phosphorus were the only nutrients which had R2 values less than 0.86 for their best calibrations. This study shows the potential to collect multiple water quality parameters at a high frequency in brackish waters using in situ spectrometers and gives the tools to replicate this analysis in all environments.

Seasonal and Inter-annual Patterns in Primary Production, Respiration, and Net Ecosystem Metabolism in Three Estuaries in the Northeast Gulf of Mexico

Measurements of primary production and respiration provide fundamental information about the trophic status of aquatic ecosystems, yet such measurements are logistically difficult and expensive to sustain as part of long-term monitoring programs. However, ecosystem metabolism parameters can be inferred from high frequency water quality data collections using autonomous logging instruments. For this study, we analyzed such time series datasets from three Gulf of Mexico estuaries: Grand Bay, MS; Weeks Bay, AL; and Apalachicola Bay, FL. Data were acquired from NOAA's National Estuarine Research Reserve System Wide Monitoring Program and used to calculate gross primary production (GPP), ecosystem respiration (ER), and net ecosystem metabolism (NEM) using Odum's open water method. The three systems represent a diversity of estuaries typical of the Gulf of Mexico region, varying by as much as two orders of magnitude in key physical characteristics, such as estuarine area, watershed area, freshwater flow, and nutrient loading. In all three systems, GPP and ER displayed strong seasonality, peaking in summer and being lowest during winter. Peak rates of GPP and ER exceeded 200 mmol O2 m−2 day−1 in all three estuaries. To our knowledge, this is the first study examining long-term trends in rates of GPP, ER, and NEM in estuaries. Variability in metabolism tended to be small among sites within each estuary. Nitrogen loading was highest in Weeks Bay, almost two times greater than that in Apalachicola Bay and 35 times greater than to Grand Bay. These differences in nitrogen loading were reflected in average annual GPP rates, which ranged from 825 g C m−2 year−1 in Weeks Bay to 401 g C m−2 year−1 for Apalachicola Bay and 377 g C m−2 year−1 in Grand Bay. Despite the strong inter-annual patterns in freshwater flow and salinity, variability in metabolic rates was low, perhaps reflecting shifts in the relative importance of benthic and phytoplankton productivity, during different flow regimes. The advantage of the open water method is that it uses readily available and cost-effective sonde monitoring technology to estimate these fundamental estuarine processes, thus providing a potential means for examining long-term trends in net carbon balance. It also provides a historical benchmark for comparison to ongoing and future monitoring focused on documenting the effect of human activities on the coastal zone.

Identification of phosphorus export from low-runoff yielding areas using combined application of high frequency water quality data and MODHMS modelling

In basins combining flat-sandy valleys and hilly-bedrock sub-catchments, the assessment of nutrient (phosphorus) exports from low-runoff yielding environments is difficult. To overcome this issue hydrological modelling and high frequency phosphorus measurements were simultaneously employed. A coupled surface water–groundwater interaction model (MODHMS) was used to determine runoff from the low-runoff yielding part of the catchment. The modelling results indicated that the lower catchment contributed less than 10% of annual catchment discharge over a number of weeks during mid-winter. High frequency phosphorus (P) measurements showed a threefold increase in P concentration during this period in 2008, which lasted for 3weeks. Concentration–discharge analysis suggested that the increase in P concentration was associated with runoff generation processes in the low-runoff yielding sub-catchment. It was estimated that this area contributed 32% of the annual P load though only 2% of total annual discharge in 2008. Both runoff and P contributions occurred during the period when the water table rose to the surface causing inundation. It was shown that the P concentrations in discharge from the low-runoff yielding sub-catchment were similar to those observed in the shallow groundwater layers.