A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Algal blooms often occur in the tidal freshwater (TF) of the James River estuary, a tributary of the Chesapeake Bay. The timing of algal blooms correlates highly to a summer low-flow period when residence time is long and nutrients are available. Because of complex interactions between physical transport and algal dynamics, it is challenging to predict interannual variations of bloom correctly using a complex eutrophication model without having ahigh-resolution model gridto resolve complexgeometryand anaccurate estimate of nutrientloading to drive the model. In this study, an approach using long-term observational data (from 1990 to 2013) and the Support vector machine (LS-SVM) for simulating algal blooms was applied. The Empirical Orthogonal Function was used to reduce the data dimension that enables the algal bloom dynamics for the entire TF to be modeled by one model. The model results indicate that the data-driven model is capable of simulating interannual algal blooms with good predictive skills and is capable of forecasting algal blooms responding to the change of nutrient loadings and environmental conditions. This study provides a link between a conceptual model and a dynamic model, and demonstrates that the data-driven model is a good approach for simulating algal blooms in this complex environment of the James River. The method is very efficient and can be applied to other estuaries as wel

Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.Comment: 32 pages, 3 figure

A Review of Harmful Algal Bloom Prediction Models for Lakes and Reservoirs

Anthropogenic activity has led to eutrophication in water bodies across the world. This eutrophication promotes blooms, cyanobacteria being among the most notorious bloom organisms. Cyanobacterial blooms (more commonly referred to as harmful algal blooms (HABs)) can devastate an ecosystem. Cyanobacteria are resilient microorganisms that have adapted to survive under a variety of conditions, often outcompeting other phytoplankton. Some species of cyanobacteria produce toxins that ward off predators. These toxins can negatively affect the health of the aquatic life, but also can impact animals and humans that drink or come in contact with these noxious waters. Although cyanotoxin’s effects on humans are not as well researched as the growth, behavior, and ecological niche of cyanobacteria, their health impacts are of large concern. It is important that research to mitigate and understand cyanobacterial blooms and cyanotoxin production continues. This project supports continued research by addressing an approach to collect and summarize published articles that focus on techniques and models to predict cyanobacterial blooms with the goal of understanding what research has been done to promote future work. The following report summarizes 34 articles from 2003 to 2020 that each describe a mechanistic or data driven model developed to predict the occurrence of cyanobacterial blooms or the presence of cyanotoxins in lakes or reservoirs with similar climates to Utah. These articles showed a shift from more mechanistic approaches to more data driven approaches with time. This resulted in a more individualistic approach to modeling, meaning that models are often produced for a single lake or reservoir and are not easily comparable to other models for different systems

Forecasting harmful algae blooms: Application to Dinophysis acuminata in northern Norway

Dinophysis acuminata produces Diarrhetic Shellfish Toxins (DST) that contaminate natural and farmed shellfish, leading to public health risks and economically impacting mussel farms. For this reason, there is a high interest in understanding and predicting D. acuminata blooms. This study assesses the environmental conditions and develops a sub-seasonal (7 - 28 days) forecast model to predict D. acuminata cells abundance in the Lyngen fjord located in northern Norway. A Support Vector Machine (SVM) model is trained to predict future D. acuminata cells abundance by using the past cell concentration, sea surface temperature (SST), Photosynthetic Active Radiation (PAR), and wind speed. Cells concentration of Dinophysis spp. are measured in-situ from 2006 to 2019, and SST, PAR, and surface wind speed are obtained by satellite remote sensing. D. acuminata only explains 40% of DST variability from 2006 to 2011, but it changes to 65% after 2011 when D. acuta prevalence reduced. The D. acuminata blooms can reach concentration up to 3954 cells l−1 and are restricted to the summer during warmer waters, varying from 7.8 to 12.7 °C. The forecast model predicts with fair accuracy the seasonal development of the blooms and the blooms amplitude, showing a coefficient of determination varying from 0.46 to 0.55. SST has been found to be a useful predictor for the seasonal development of the blooms, while the past cells abundance is needed for updating the current status and adjusting the blooms timing and amplitude. The calibrated model should be tested operationally in the future to provide an early warning of D. acuminata blooms in the Lyngen fjord. The approach can be generalized to other regions by recalibrating the model with local observations of D. acuminata blooms and remote sensing data.publishedVersio

An Overview of Cyanobacteria Harmful Algal Bloom (CyanoHAB) Issues in Freshwater Ecosystems

This chapter will present an overview of cyanobacterial harmful algal blooms (cyanoHABs) and biotic and abiotic factors, as well as various aspects associated with these worldwide ecological bursts. The exact causes of the cyanoHABs are still not well defined, but eutrophication and climate change (temperature increase, light intensity variation, etc.) are the two assumed main factors that may promote the proliferation and expansion of cyanobacterial blooms. However, these premises need to be profoundly investigated as the optimal combination of all factors such as increased nutrient loading, physiological characteristics of cyanobacterial species, and climate effects which could lead to the blooming pattern will require robust modeling approaches to predict the phenomena. Negative issues associated with cyanoHABs are diverse including the toxic products (cyanotoxins) released by certain taxa which can damage the health of humans and animal habitats around the related watershed as well as generate a huge water quality problem for aquatic industries

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

A Machine Learning Approach to Sentinel-3 Feature Extraction In The Context Of Harmful Algal Blooms

Harmful Algal Blooms (HAB) are typically described as blooms of phytoplankton species that can not only cause harm to the environment but also humans. Some species that form these blooms can release biotoxins, which accumulate in shellfish [1]. When humans consume contaminated shellfish, it can cause adverse health problems [2]–[4]. Due to the associated risk of contamination, shellfisheries are forced to close, sometimes for months, leading to significant economic losses. Although microscopes enable toxic species identification, and bioassays enable biotoxin identification and quantification, these methods are impractical for continuous monitoring since they require recurrent in situ data sampling, followed by laboratory analysis. Chlorophyll a is a pigment common to almost all marine phytoplankton groups. It has a spectral signature that enables it to be detectable by remote satellites that capture water-leaving radiance [5]. Remote sensing can be very useful since it allows us to take synoptic measurements of large sea areas [6]. Several machine learning algorithms have been researched to detect or forecast algal biomass or HAB presence [7]–[10]. However, the application of remotely sensed images to detect and forecast biotoxin concentration seems relatively unexplored. Given this problem, two datasets with Sentinel-3 imagery patches were created, from along the west coastal region of Portugal, which differ in size and the preprocessing applied. We assessed the application of Machine Learning (ML) models to extract informative features from the datasets. The models were evaluated quantitatively and qualitatively. The qualitative analysis demonstrated how the features extracted by the models seem to be consistent with features extracted for downstream tasks in the literature, suggesting the features retain helpful information. However, at this time, further work Is required to determine whether the feature can be helpful in the task of biotoxin concentration forecasting.Um Harmful Algal Bloom (HAB) é tipicamente descrito como sendo a proliferação de espécies de fitoplâncton que podem causar danos não só ao ambiente, mas também aos humanos. Algumas espécies que formam HABs podem libertar biotoxinas, que se acumulam nos moluscos [1]. Quando o ser humano consome moluscos contaminados, pode causar problemas de saúde adversos [2]–[4]. Devido ao risco associado de contaminação, as áreas de exploração de bivalves são forçadas a fechar, por vezes durante meses, levando a perdas económicas significantes. A clorofila a é um pigmento comum a quase todos os grupos de fitoplâncton marinho e tem uma assinatura espectral que lhe permite ser detectável por satélites remotos que captam a radiância que sai da água do mar [5]. A detecção remota pode ser muito útil, uma vez que nos permite fazer medições sinópticas de grandes áreas marítimas [6]. Foram pesquisados vários modelos de aprendizagem automática para detectar ou prever a presença de biomassa algal ou HAB [7]–[10]. No entanto, a utilização de imagens de detecção remota para detectar e prever a concentração de biotoxinas parece relativamente inexplorada. Dado este problema, foram criados dois conjuntos de dados com patches de imagens do satélite Sentinel-3 ao longo da região costeira ocidental de Portugal, que diferem em tamanho e no pré-processamento aplicado. Avaliámos diferentes modelos de aprendizagem automática para extrair características informativas dos conjuntos de dados. Os modelos foram avaliados quantitativa e qualitativamente. A análise qualitativa demonstrou como a informação extraída pelos modelos parecem ser consistentes com a extraída na literatura para informar outros modelos, sugerindo que as características retêm informação útil. Contudo, neste momento, é necessário trabalho futuro para determinar se a informação pode ser útil na tarefa de previsão da concentração de biotoxinas