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  1. The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems.

     
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    Free, publicly-accessible full text available August 1, 2024
  2. Abstract

    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 CO2increases 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 chlorophylla(Chla) during the spring in mid to lower estuarine regions indicative of strong control by net phytoplankton production. During summer and fall, Chlaand 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 CO2rich bottom waters and post‐storm terrestrial organic matter loading. Local processes we highlight represent a significant challenge for predicting future estuarine pH.

     
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  3. null (Ed.)
    Estuaries function as important transporters, transformers, and producers of organic matter (OM). Along the freshwater to saltwater gradient, the composition of OM is influenced by physical and biogeochemical processes that change spatially and temporally, making it difficult to constrain OM in these ecosystems. In addition, many of the environmental parameters (temperature, precipitation, riverine discharge) controlling OM are expected to change due to climate change. To better understand the environmental drivers of OM quantity (concentration) and quality (absorbance, fluorescence), we assessed both dissolved OM (DOM) and particulate OM (POM) spatially, along the freshwater to saltwater gradient and temporally, for a full year. We found seasonal differences in salinity throughout the estuary due to elevated riverine discharge during the late fall to early spring, with corresponding changes to OM quantity and quality. Using redundancy analysis, we found DOM covaried with salinity (adjusted r2 = 0.35, 0.41 for surface and bottom), indicating terrestrial sources of DOM in riverine discharge were the dominant DOM sources throughout the estuary, while POM covaried with environmental indictors of terrestrial sources (turbidity, adjusted r2 = 0.16, 0.23 for surface and bottom) as well as phytoplankton biomass (chlorophyll-a, adjusted r2 = 0.25, 0.14 for surface and bottom). Responses in OM quantity and quality observed during the period of elevated discharge were similar to studies assessing OM quality following extreme storm events suggesting that regional changes in precipitation, as predicted by climate change, will be as important in changing the estuarine OM pool as episodic storm events in the future. 
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  4. null (Ed.)
  5. Coastal ecosystems display consistent patterns of trade-offs between resistance and resilience to tropical cyclones. 
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  6. Abstract Tropical cyclones play an increasingly important role in shaping ecosystems. Understanding and generalizing their responses is challenging because of meteorological variability among storms and its interaction with ecosystems. We present a research framework designed to compare tropical cyclone effects within and across ecosystems that: a) uses a disaggregating approach that measures the responses of individual ecosystem components, b) links the response of ecosystem components at fine temporal scales to meteorology and antecedent conditions, and c) examines responses of ecosystem using a resistance–resilience perspective by quantifying the magnitude of change and recovery time. We demonstrate the utility of the framework using three examples of ecosystem response: gross primary productivity, stream biogeochemical export, and organismal abundances. Finally, we present the case for a network of sentinel sites with consistent monitoring to measure and compare ecosystem responses to cyclones across the United States, which could help improve coastal ecosystem resilience. 
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