Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity

Katin, Alexey; Giudice, Dario Del; Hall, Nathan S.; Paerl, Hans W.; Obenour, Daniel R.

doi:10.1016/j.ecolmodel.2021.109497

Cited by 6 publications

(3 citation statements)

References 105 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At temperate latitudes, net autotrophy of surface waters in spring is common (Carstensen and Duarte 2019) because much of the respiration of the spring bloom organic matter occurs in bottom waters later in summer (Carstensen and Duarte 2019). In both estuaries, the intensity of the spring bloom is strongly regulated by the dual roles of river flow in delivering nutrients and flushing effects on phytoplankton biomass (Harding et al 2002; Katin et al 2021). In CB, the spring bloom was most intense when high flows in late winter led to strong nutrient loading followed by low flows during spring, which allowed sufficiently long residence time for phytoplankton biomass to develop (Harding et al 2002).…”

Section: Resultsmentioning

confidence: 99%

Assessing drivers of estuarine pH: A comparative analysis of the continental U.S.A.'s two largest estuaries

Hall,

Testa,

et al. 2023

Limnology & Oceanography

View full text Add to dashboard Cite

In estuaries, local processes such as changing material loads from the watershed and complex circulation create dynamic environments with respect to ecosystem metabolism and carbonate chemistry that can strongly modulate impacts of global atmospheric CO2 increases on estuarine pH. Long‐term (> 20 yr) surface water pH records from the USA's two largest estuaries, Chesapeake Bay (CB) and Neuse River Estuary‐Pamlico Sound (NRE‐PS) were examined to understand the relative importance of atmospheric forcing vs. local processes in controlling pH. At the estuaries’ heads, pH increases in CB and decreases in NRE‐PS were driven primarily by changing ratios of river alkalinity to dissolved inorganic carbon concentrations. In upper reaches of CB and middle reaches of the NRE‐PS, pH increases were associated with increases in phytoplankton biomass. There was no significant pH change in the lower NRE‐PS and only the polyhaline CB showed a pH decline consistent with ocean acidification. In both estuaries, interannual pH variability showed robust, positive correlations with chlorophyll a (Chl a) during the spring in mid to lower estuarine regions indicative of strong control by net phytoplankton production. During summer and fall, Chl a and pH negatively correlated in lower regions of both estuaries, given a shift toward heterotrophy driven by changes in phytoplankton community structure and increases in the load ratio of dissolved inorganic nitrogen to organic carbon. Tropical cyclones episodically depressed pH due to vertical mixing of CO2 rich bottom waters and post‐storm terrestrial organic matter loading. Local processes we highlight represent a significant challenge for predicting future estuarine pH.

show abstract

Section: Resultsmentioning

confidence: 99%

Assessing drivers of estuarine pH: A comparative analysis of the continental U.S.A.'s two largest estuaries

Hall,

Testa,

et al. 2023

Limnology & Oceanography

View full text Add to dashboard Cite

show abstract

“…The importance of understanding temporal variability in nutrient export and retention is further highlighted by the potential for climate change to increase precipitation intensity, potentially leading to more severe eutrophication (Sinha et al., 2017). Results indicate that older urban developments need to be managed (along with point sources) if water quality problems are most prevalent under low‐flow conditions, as is sometimes the case for harmful algal blooms (Katin et al., 2021; Michalak et al., 2013). On the other hand, agricultural export becomes a large TP contributor in high flow years, particularly for the HR and FL subbasins (Figure 6), which remain predominantly rural despite considerable urban development (Text S2 in Supporting Information ).…”

Section: Discussionmentioning

confidence: 99%

Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model

Karimi

Miller

Sankarasubramanian

et al. 2023

Water Resources Research

View full text Add to dashboard Cite

Nutrient pollution is a major threat to water quality in the United States (Whitall et al., 2007) and worldwide (Woodward et al., 2012). It can result in algal blooms, hypoxia, and undesirable ecosystem shifts, jeopardizing economies dependent on affected natural resources (Brooks et al., 2016;Conley et al., 2009). Phosphorus is recognized to be the primary limiting nutrient of eutrophication for many lakes and reservoirs (Carpenter, 2008;Schindler et al., 2016), though nitrogen may also be important in some freshwater systems (Paerl et al., 2016). Nutrient sources typically include wastewater effluent from point sources, plus urban and agricultural nonpoint sources (Davidson et al., 2014;Howarth et al., 2002). While there has been considerable success in regulating point sources, less progress has been made toward reducing diffuse nonpoint fluxes (

show abstract

“…Monitoring strategies for HABs often entail laboratory analysis of water samples and quantification of various algal-related parameters such as chlorophyll-a (Katin et al, 2021), phycocyanin (Giere et al, 2020), cyanobacteria or other algal cells (Ponjavić et al, 2019), and various algal toxins (Greer et al, 2016). The analyses include microscopic and analytical approaches using a spectrophotometer, liquid chromatography, whole-organism bioassays, and biochemical and immunological assays (Lombard et al, 2019).…”

mentioning

confidence: 99%

Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models

Busari,

Sahoo,

Harmel

et al. 2024

Journal of Natural Resources and Agricultural Ecosystems

View full text Add to dashboard Cite

Highlights The monitoring of HABs can be improved using ML models for chlorophyll-a prediction. ML model selection for HABs monitoring depends on target objectives. Random forest model predicts chlorophyll-a better when the temporal dimension is not considered. The LSTM model is essential for making time-dependent chlorophyll-a predictions for HABs monitoring. Abstract. The complex dynamics of freshwater harmful algal blooms (HABs) necessitate proactive monitoring approaches to mitigate their impacts. The rapid breakthrough in computing prowess and statistical advances is triggering the development of data-driven techniques such as machine learning (ML) models, which have been shown in different fields to be instrumental in finding patterns for explaining relationships in observed data. This study assesses the ability of ML models for HABs monitoring in a lake using chlorophyll-a concentration as the index. The selected models for this study were regression tree, random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent unit (GRU) models, with the last two models able to consider the temporal sequence of obtained water quality datasets. The results showed that the RF model with R2, mean absolute error (MAE), and root mean square error (RMSE) of 0.87 µgL-1, 0.97 µgL-1, and 3.53 µgL-1, respectively, outperformed the SVR, MLP, and regression tree models. LSTM model with MAE and RMSE of 2.39 µgL-1 and 3.29 µgL-1, respectively, predicted temporal dynamics of chlorophyll-a better than GRU, although with more runtime, and showed the potential for developing real-time HAB monitoring and early warning systems. The findings reveal the robustness of the chosen ML models, thereby shedding light on crucial factors that necessitate careful deliberation by researchers and policymakers in determining the most suitable approaches for monitoring HABs. Keywords: Cyanobacteria, Early warning systems, Freshwater, HABs, Machine learning models.

show abstract

Simulating algal dynamics within a Bayesian framework to evaluate controls on estuary productivity

Cited by 6 publications

References 105 publications

Assessing drivers of estuarine pH: A comparative analysis of the continental U.S.A.'s two largest estuaries

Assessing drivers of estuarine pH: A comparative analysis of the continental U.S.A.'s two largest estuaries

Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model

Prediction Of Chlorophyll-a As an Index of Harmful Algal Blooms Using Machine Learning Models

Contact Info

Product

Resources

About