Bloom Events Research Articles

An accidental encounter with bloom of diatom Palmerina hardmaniana (Greville) Hasle in Sundarbans, India: Impact on zooplankton community structure in tidal creek environment

The present study reports on a transient diatom bloom of Palmerina hardmaniana (Greville) Hasle 1996 encountered accidentally in May 2018 in the world's largest mangrove forest of Sundarbans, India. This bloom was the sixth record of diatom blooms from Indian coastal waters and the fourth from Sundarbans waters. The diatom cell count ranged from 2.571 × 104 to 6.857 × 104 cells L−1 during the bloom. The zooplankton taxa count decreased from 39 during the pre-bloom to 30 during bloom and 31 during the post-bloom phase. However, no significant (p < 0.05) change in zooplankton species richness was observed between the three different phases. The consistent occurrence of copepod Bestiolina simlis across sampling points indicated a high tolerance to environmental parameters. The zooplankton population also did not respond vehemently to bloom-forming P. hardmaniana in this creek environment. These observations warrant further studies to examine the effect of blooms on the overall marine ecological food chain of creek ecosystems.

Distortion of Bessel Gaussian beams caused by atmospheric thermal blooming effect

Distortion of Bessel Gaussian beams caused by atmospheric thermal blooming effect

Thermal Blooming Effect of Power-Exponent-Phase Vortex Beams Propagating through the Atmosphere

The thermal blooming effect of power-exponent-phase vortex (PEPV) beam propagating in the atmosphere is investigated by employing the multiple phase screen method. The influences of propagation distance, topological charge, power exponent, wind speed, and absorption coefficient on thermal blooming effect are analyzed in detail. The results show that (1) the thermal blooming effect exhibits a significant perturbation on the intensity and phase distribution of PEPV beams, with its influence becoming more pronounced as the propagation distance increases; (2) when the power exponent is fixed at 4, comparing the thermal blooming effect of PEPV beams with different topological charges indicates that a PEPV beam with topological charges of 3 is the most sensitive to thermal blooming; (3) when the topological charge is fixed at 3, the thermal blooming effect decreases with the increase in power exponent; and (4) an increase in wind speed or a decrease in absorption coefficient can reduce the thermal blooming effect. The research results obtained in this article have guiding significance for the application of research into high-energy PEPV beams in the atmosphere.

Noctiluca scintillans bloom alters the composition and carbohydrate utilization of associated bacterial community and enriches potential pathogenic bacterium Vibrio anguillarum

Noctiluca scintillans (red) is a widely distributed heterotrophic dinoflagellate and a prominent red tide forming species. This study investigated the effects of Noctiluca blooms on marine microbial diversity and functionality using multi-omics approaches. Our findings revealed significant differences in the community composition of Noctiluca-associated bacteria compared to those associated with autotrophic plankton and free-living bacteria in the surrounding seawater. The dominant bacterial groups within the Noctiluca-associated community shifted at various bloom stages, which could be attributed to changes in prey composition of Noctiluca. During the non-bloom stage, Burkholderiaceae, Carnobacteriaceae, and Pseudomonadaceae dominated the community, while Vibrionaceae became dominant during the bloom stage, and Saprospiraceae, Crocinitomicaceae, and Pirellulaceae thrived during the post-bloom stage. Compared to the non-bloom stage, Noctiluca-associated bacterial community at the bloom stage exhibited significant down-regulation of genes related to complex carbohydrate metabolism, while up-regulation of genes related to glucose transportation and utilization. Furthermore, we identified Vibrio anguillarum, a potential pathogenic bacterium to marine fish, as a major component of the Vibrionaceae family during the bloom stage. The occurrence of V. anguillarum associated with Noctiluca blooms may be attributed to the increased availability of its preferred carbon sources and its high capabilities in glucose transportation, motility and chemotaxis. Moreover, the presence of Vibrio infection genes (hap, hlyA, rtxA) encoding vibriolysin, hemolysin, and RTX (Repeats-in-toxin) toxin in the V. anguillarum genome, with the hap gene showing high expression levels during Noctiluca blooms, indicates an elevated risk of infection. This study underscores the unique composition of the bacterial community associated with red tide forming heterotrophic dinoflagellates and suggests that Noctiluca cells may serve as reservoirs and vectors for pathogenic bacteria, potentially posing a threat to fish-farming and the health of other marine organisms.

Quantifying the linkages between California sea lion (Zalophus californianus) strandings and particulate domoic acid concentrations at piers across Southern California

Domoic acid-producing blooms of the diatom genus Pseudo-nitzschia are pervasive in coastal environments globally. Domoic acid, a neurotoxin, accumulates via trophic transfer into marine food webs and is often associated with mass marine mammal mortality and stranding events. In Southern California, California sea lions (Zalophus californianus) are an indicator species for food web impacts of domoic acid because they are abundant secondary consumers, sensitive to domoic acid intoxication, and are actively monitored by stranding networks. However, domoic acid exposure may occur a distance from where a sea lion ultimately strands. This spatiotemporal variation complicates coupling domoic acid observations in water to strandings. Therefore, we sought to quantify whether monitoring data from four pier sites across the region, covering nearly 700 km of coastline from 2015-2019, could be used to predict adult and subadult sea lion strandings along the 68 km Orange County coastline surveyed by the Pacific Marine Mammal Center. We found that increased sea lion strandings were often observed just prior to an increase in particulate domoic acid at the piers, confirming that clusters of subadult and adult sea lion strandings with clinical signs of domoic acid intoxication serve as indicators of bloom events. In addition, domoic acid concentrations at Stearns Wharf, nearly 200 km from stranding locations, best predicted increased total sea lion strandings, and strandings of sea lions with domoic acid intoxication symptoms. Particulate domoic acid concentrations greater than 0.05 μg/L at Stearns Wharf were linked to stranding probabilities in Orange County ranging from 2.2% to 55% per week, and concentrations of 0.25 μg/L resulted in weekly stranding probabilities ranging from 16% to 81% depending on the stranding scenario modeled.

Physiology governing diatom vs. dinoflagellate bloom and decline in coastal Santa Monica Bay

Algal blooms on the Southern California coast are typically dominated by diatom and dinoflagellate taxa, and are governed by their physiological responses to environmental cues; however, we lack a predictive understanding of the environmental controls underlying the establishment and persistence of these distinct bloom events. In this study, we examined gene expression among the numerically dominant diatom and dinoflagellate taxa during spring upwelling bloom events to compare the physiological underpinnings of diatom vs. dinoflagellate bloom dynamics. Diatoms, which bloomed following upwelling events, expressed genes related to dissolved inorganic nitrogen utilization, and genes related to the catabolism of chitin that may have prolonged their bloom duration following nitrogen depletion. Conversely, dinoflagellates bloomed under depleted inorganic nitrogen conditions, exhibited less variation in transcriptional activity, and expressed few genes associated with dissolved inorganic nutrients during their bloom. Dinoflagellate profiles exhibited evidence of proteolysis and heterotrophy that may have enabled them to bloom to high abundances under depleted inorganic nutrients. Taken together, diatom and dinoflagellate transcriptional profiles illustrated guild-specific physiologies that are tuned to respond to and thrive under distinct environmental “windows of opportunity.”

Thermal effect of high-power laser propagation in complex channels and its mitigation

We investigate the channel thermal effect resulting from the gas thermal blooming effect and mirror thermal deformation on the beam quality, and we propose a mitigation measure for blowing purge gases. Without gas blowing, the gas thermal blooming effect opposes the influence of mirror thermal deformation on the beam phase, resulting in significant phase distortion and offset. Noticeable distortion and divergence of the laser spot was also observed. After blowing purge gases, the beam phase was primarily determined by the mirror thermal deformation. Simultaneously, the mean-squared beam width decreased uniformly, improving the symmetry and energy concentration of the laser spot. However, the laser spot tended to stabilize when the gas velocity exceeded a certain threshold.

Phosphonate consumers potentially contributing to methane production in Brazilian soda lakes.

Oxic methane production (OMP) has been reported to significantly contribute to methane emissions from oxic surface waters. Demethylation of organic compounds, photosynthesis-associated methane production, and (bacterio)chlorophyll reduction activity are some of the investigated mechanisms as potential OMP sources related to photosynthetic organisms. Recently, cyanobacteria have often been correlated with methane accumulation and emission in freshwater, marine, and saline systems. The Brazilian Pantanal is the world's largest wetland system, with approximately 10,000 shallow lakes, most of which are highly alkaline and saline extreme environments. We initiated this study with an overall investigation using genetic markers, from which we explored metagenomic and limnological data from the Pantanal soda for five potential OMP pathways. Our results showed a strong positive correlation between dissolved methane concentrations and bloom events. Metagenomic data and nutrients, mainly orthophosphate, nitrogen, iron, and methane concentrations, suggest that the organic phosphorous demethylation pathway has the most potential to drive OMP in lakes with blooms. A specialized bacterial community was identified, including the Cyanobacteria Raphidiopsis, although the bloom does not contain the genes to carry out this process. These data showed enough evidence to infer the occurrence of an OMP pathway at Pantanal soda lakes, including the microbial sources and their relation to the cyanobacterial blooms.

An Assessment of Potential Threats to Human Health from Algae Blooms in the Indian River Lagoon (USA) 2018-2021: Unique Patterns of Cytotoxicity Associated with Toxins.

The Indian River Lagoon (IRL), a 156-mile-long estuary located on the eastern coast of Florida, experiences phytoplankton bloom events due to increased seasonal temperatures coupled with anthropogenic impacts. This study aimed to gather data on the toxicity to human cells and to identify secondary metabolites found in water samples collected in the IRL. Water samples from 20 sites of the IRL were collected during the wet and dry seasons over a three-year period. A panel of cell lines was used to test cytotoxicity. Hemagglutination, hemolysis, and inhibition of protein phosphatase 2A (PP2A) were also measured. Cytotoxic blooms were seen both in the south (Microcystis) and the north (Pyrodinium) of the IRL. Each toxin induced a consistent pattern of cytotoxicity in the panel of human cell lines assayed. During blooms, cytotoxicity due to a single type of toxin is obvious from this pattern. In the absence of blooms, the cytotoxicity seen reflected either a mixture of toxins or it was caused by an unidentified toxin. These observations suggest that other toxins with the potential to be harmful to human health may be present in the IRL. Moreover, the presence of toxins in the IRL is not always associated with blooms of known toxin-producing organisms.

Cloud System Structure Pre-Warning System and Method for Canal type Reservoir Tributary Bay Water Bloom

This research proposes a cloud system structure pre-warning system and method for canal type reservoir tributary bay water bloom. The system consists of five modules: monitoring cloud module, information cloud module, application supporting module, water bloom pre-warning module, and discussion decision-making module. The system achieves seamless linkage and operation control based on a cloud system structure, carrying out all-weather monitoring, cloud storage, and water bloom risk pre-warning. The water bloom pre-warning module consists of a risk analysis unit and a pre-warning and forecasting unit. The discussion decision-making module is used for achieving decision-making discussion and information sharing communication in a cloud environment. This study provides a new measure based on the cloud system structure for preventing and reducing water bloom events.

Bloom and Postbloom Thinner Effects and Interactions on ‘Gala’ Fruit Growth Rate, Return Bloom, and Yield Responses at Three Locations

Multistep chemical thinning programs have been widely recommended in the eastern United States; however, adoption of bloom thinners is limited. With caustic blossom thinners, narrow effective application timings and concerns related to spring frost damage are barriers for commercial use in this region. If effective and safe, use of hormonal blossom thinners for apple would be an attractive alternative. We evaluated the effects and interactions of bloom thinners [6-benzyladenine (BA) and lime sulfur (LS, or calcium polysulfide) + stylet oil (LS+SO)] and a postbloom thinner (NAA) in the context of a multistep, carbaryl-free thinning program across three locations. Experiments were conducted in 2017 and 2018 on mature ‘Gala’ in North Carolina, Massachusetts, and Pennsylvania, USA. In four of six studies, BA at bloom increased the efficacy of postbloom NAA and reduced crop density (P &lt; 0.08). Postbloom NAA generally increased fruit relative growth rate (RGR) and reduced crop density. However, where NAA failed to reduce crop load, there was a negative influence on RGR. BA and LS+SO increased RGR in one of six studies; however, BA was generally ineffective as a blossom thinner, whereas LS+SO was more effective. Nevertheless, BA applied at bloom may have utility as part of a multistep thinning program. As a part of a multistep thinning program, BA applied at bloom may be useful in increasing efficacy of postbloom applications, particularly when use of caustic blossom thinners is not permitted.

Decoding the influence of bacterial community structure and algicidal bacteria in a Karenia longicanalis bloom event

IntroductionHarmful algal blooms (HABs) have been increasing in frequency and expanding their ranges on coastlines worldwide in recent decades. Algicidal bacteria play a pivotal role in eliminating HABs, yet the characteristics of bacterial communities and their algicidal activity during a Karenia longicanalis bloom remain poorly understood.MethodsIn this study, we investigated bacterial communities using 16S rRNA sequencing during a K. longicanalis bloom to identify bacteria with high algicidal activity that could be isolated. Five sampling sites in Tongxin Bay, located in Lianjiang County, China, including TX1 to TX5, were selected based on the concentration of K. longicanalis cells.ResultsOur 16S rRNA sequencing results revealed that the TX4 site was enriched with genera known to contain algicidal bacteria, such as Pseudoalteromonas and Alteromonas, which are members of the Gammaproteobacteria class, while Sulfitobacter, a member of the Alphaproteobacteria class, was enriched in the TX5 site. Among the 100 cultivable bacteria isolated from the 5 sampling sites, 6 exhibited an algicidal rate of over 80%, with FDHY-MQ5, isolated from the TX4 site, exhibiting an algicidal rate of approximately 100% against Karenia mikimotoi after 48 hours of challenge with 2% (v/v) bacterial volume (OD600=4.5) concentration. Our 16S rRNA sequencing result showed FDHY-MQ5 was a member of the Pseudoalteromonas genus, and this bacterium also demonstrated high algicidal activity against Heterosigma akashiwo and Alexandrium tamarense.DiscussionOur findings shed light on the changes in bacterial community structure and the algicidal behavior of bacteria towards algae during a K. longicanalis bloom, providing a research basis for a better understanding of HAB management.

The Dynamics of Floating Macroalgae in the East China Sea and Its Vicinity Waters: A Comparison between 2017 and 2023

Ulva prolifera and Sargassum are two common floating macroalgae in China’s coastal algal bloom events. Ulva prolifera frequently emerges concomitantly with Sargassum outbreaks, thereby presenting challenges to the monitoring of algal blooms, thereby presenting challenges to the monitoring of algae. To tackle the challenge of differentiating between Ulva prolifera and Sargassum, this study employs Sentinel-2 MSI data for spectral analysis. Notably, significant disparities in the Remote Top of Atmosphere Reflectance (Rtoa) between Ulva prolifera and Sargassum are observed. This study proposes a random forest-based algorithm for discriminating between Ulva prolifera and Sargassum in the regions of the Yellow Sea and East China Sea. The algorithm introduced in this study attains remarkable accuracy in distinguishing Ulva prolifera and Sargassum within Sentinel-2 MSI data, achieving identical F1 scores of 99.1% for both. Moreover, when tested with GF-1 WFV data, the algorithm showcases outstanding performance; this demonstrates the algorithm’s robustness and its ability to mitigate the uncertainty linked to threshold selection. Simultaneously, a comparative analysis of algae distribution was conducted for both 2017 and the period from January to May 2023. Experimental results indicate that the algorithm exhibits high accuracy in distinguishing between Ulva prolifera and Sargassum. This capability will significantly enhance the monitoring of large algae in maritime regions; this holds crucial theoretical significance and offers substantial practical value in the realm of marine ecological conservation.

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

Over the past few decades, harmful algal blooms (HABs) have occurred frequently worldwide. The application of harmful algal bloom detection when based solely on water quality measurements proves challenging in achieving broad generalization across various regions. Satellite remote sensing, due to its low risk, cost effectiveness, and wide ground-coverage capabilities, has been extensively employed in HAB detection tasks. However, relying solely on remote sensing data poses issues of false positives, false negatives, and the incomplete consideration of contributing factors in HAB detection. This study proposes a model for harmful algal bloom detection by integrating MODIS multifactor data with heterogeneous meteorological data. Initially, a dataset named MODIS_MI_HABs is constructed by gathering information from 192 instances of harmful algal bloom events worldwide. Subsequently, remote sensing data corresponding to specific regions are collected; all were obtained from a moderate resolution imaging spectroradiometer (MODIS) aboard an ocean-color-detecting satellite. This dataset encompasses variables such as chlorophyll-a concentration, the sea surface temperature, photosynthetically active radiation, the relative radiation stability differences, the six seawater-absorption coefficients, and three scattering coefficients. By fusing six meteorological factors, latitude and longitude information, and remote sensing data, a regression dataset for harmful algal bloom detection is established. Finally, employing harmful algal bloom cell concentration as the data label, seven machine learning models are employed to establish correlations between the remote sensing data, heterogeneous meteorological data, and harmful algal bloom cell concentrations. The root mean square error (RMSE), mean absolute error (MAE), explained variance (EV), and coefficient of determination (R2) parameters are used to evaluate the regression performance. The results indicate that the extreme gradient boosting (XGR) model demonstrates the best predictive capability for harmful algal blooms (leave-one-out: RMSE/MAE = 0.0714). The XGR model, trained with the entire dataset, yields the optimal predictive performance (RMSE = 0.0236, MAE = 0.0151, EV = 0.9593, R2 = 0.9493). When compared to the predictions based on the fixed-area water quality analysis and single-source remote sensing data usage, the proposed approach in this paper displays wide applicability, offering valuable support for the sustainable development of marine ecology.

A dinoflagellate bloom caused by multiple species of Kareniaceae in the coastal waters of Fujian in June 2022 and its adverse impacts on Brachionus plicatilis and Artemia salina

Recently, dinoflagellate blooms have frequently occurred in the coastal waters of Fujian, East China Sea. In June 2022, a fish-killing bloom of Kareniaceae species occurred in this region. In this study, four species of Kareniaceae, namely, Karenia longicanalis, K. papilionacea, Karlodinium veneficum, and Karl. digitatum were identified from this bloom event based on the results of single-cell PCR and clone libraries, and intraspecies genetic diversity was found in the Karl. veneficum population. The results of acute toxicity assays of the bloom water to two zooplankton species (Brachionus plicatilis and Artemia salina) demonstrated this bloom event strongly inhibited their swimming capacities and survival. The results of this study suggested that the bloom events caused by multiple species of Kareniaceae in the Fujian coastal waters had adverse impacts on the local fishery resources and zooplankton community.

Prediction of algal bloom using a combination of sparse modeling and a machine learning algorithm: Automatic relevance determination and support vector machine

Algae can produce odor substances and toxins that make the smell and taste of water unpleasant and impair the quality of human and aquatic life. Appropriate countermeasures can be implemented in advance in water purification processes to prevent algal disorders if the occurrence of algal blooms is accurately predicted. Several models have been developed to predict algal blooms. However, a comprehensive model that can be universally applied under various conditions is lacking. In this study, automatic relevance determination, a sparse modeling algorithm, and support vector machine were combined to construct prediction models for algal blooms in four Japanese dam reservoirs to predict their occurrence over 7 days. Automatic relevance determination was applied to a dataset consisting of monthly water quality data and daily hydraulic and meteorological data to identify variables relevant to the concentrations of Microcystis spp. and Dolichospermum spp., which are bloom-forming cyanobacteria and are dominant in freshwater ecosystems. A dataset of selected variables was used to train and validate the support vector machine models. The results of variable selection by automatic relevance determination revealed that the average concentration of total nitrogen in the past year and the average maximum temperature in the past 7 days may have an association with the algal concentration. Support vector machine models resulted in 92.3% accuracy and 86.4% precision for Microcystis spp. and 71.4% accuracy and 77.5% precision for Dolichospermum spp. on average in binary classification. The competitive relationship between Microcystis spp. and Dolichospermum spp., which differs according to the nutrient level and temperature, probably affects the prediction performance of the models. Our study suggests that the combination of sparse modeling and machine learning is applicable to the construction of a prediction model for site-specific algal bloom events in dam reservoirs.

Enhanced degradation of methionine by eco-friendly and low-cost tannin extract modified Fenton oxidation: Promotion of the Fe(III)/Fe(II) cycle

In this study, a low-cost natural biomass extracted from higher plants - Tannin - was used to improve the Fenton system by promoting iron cycling due to the abundant phenolic hydroxyl groups in the molecular structure. Tannin extract (TE) of black wattle was combined with Fe(Ⅱ)/H2O2 system at room temperature and under pH 3 to 8. Methionine (Met) was chosen as a target pollutant, as its critical role in the global sulfur cycle and algae-induced black bloom events. The results found that TE could significantly promote Fe(Ⅱ)/H2O2 system, Met degradation increased from 44 % to 86 % under pH 6.0, and the mineralization increased from 14.86 % to 38.96 %. Both quenching experiments and electron paramagnetic resonance (EPR) analysis confirmed that OH and O2− contributed to Met degradation, and OH played a key role in TE/Fe(Ⅱ)/H2O2 system. The reducibility of TE at different pH values was evaluated, the highest Fe(Ⅲ) reducing rate of TE was obtained at pH of 4.5 (k = 22.48 M−1min−1) based on the formation model. TE and its complex catalyzed the dismutation of O2− to form OH. The degradation rate constant of TE/Fe(Ⅱ)/H2O2 system was 3.63 min−1, and the intermediates of Met degradation was proved to be non-toxic. In conclusion, TE could facilitate the iron recycle in Fenton system, consequently to strengthen H2O2 activation, and it offered an energy-saving and eco-friendly way for organic pollutants treatment.

MAREL Carnot data and metadata from the Coriolis data center

Abstract. The French coast of the eastern English Channel (ECC) is classified as a potential eutrophication zone by the Oslo and Paris (OSPAR) Convention and as moderate to poor according to the phytoplankton quality element of the Water Framework Directive (WFD). It is regularly affected by Phaeocystis globosa bloom events, which have detrimental effects on the marine ecosystem, economy, and public health. In this context and to improve our observation strategy, MAREL Carnot, a multi-sensor oceanographic station, was installed in the eastern English Channel in 2004 at the Carnot wall in Boulogne-sur-Mer. The aim of this station was to collect high-frequency measurements of several water quality parameters to complement conventional low-resolution monitoring programs. The purpose of this paper is to describe the MAREL Carnot dataset and show how it can be used for several research objectives. MAREL Carnot collects high-frequency, multi-parameter observations from surface water as well as meteorological measurements and sends the data in near real-time to an onshore data center. In this paper, we present several physical, chemical, and biological parameters measured by this station. We also demonstrate that the MAREL Carnot dataset can be used to assess environmental or ecological statuses and conduct research in the field of marine phytoplankton ecology and oceanography. In addition, we show that this dataset may indirectly aid in improving European environmental management strategies. The MAREL Carnot dataset is publicly accessible via https://doi.org/10.17882/39754 (MAREL Carnot, 2023).

Mapping cherry blossom phenology using a semi-automatic observation system with street level photos

Floral phenology is a key indicator of climate change. However, constructing a local level spatiotemporal record of cherry blossom blooming events is challenging because of the limited coverage of traditional stationary observations. To address this issue, citizen-based observation programs and remote sensing applications have been implemented. However, these strategies have still been limited by insufficient spatial and temporal observation data. In this study, we introduced a novel semi-automatic observation system framework for cherry blossom blooming by combining geographically and temporally dispersed street-level imagery and deep-learning models. We developed a YOLOv4 model for cherry blossom detection from street-level photos obtained from Mapillary, one of the main social sensing data repositories. The detection model achieved an overall accuracy, recall, and precision of 86.7%, 70.3%, and 90.1%, respectively. By using observation coordinates and dates attached to Mapillary photos, we mapped the probability of cherry blossoms occurrence daily using a spatial grid of dimensions of 10 m × 10 m. With sufficient observations, the start, peak, and cessation of blooming were estimated using time series analysis. A case study conducted in 2022 at the main campus of Saitama University confirmed the potential for automatically mapping the presence of cherry blossoms and their blooming timing with local variations. Given that our approach solely relied on geotagged street-level photos that could be taken by anyone with no prior knowledge of cherry tree species identification, it is likely to be easier to build spatial and temporal blooming records than using conventional stationary or citizen-based observations. This approach can be applicable to other study areas, as well as observing other species phenology and understanding the response of local environments.

Bio-optical properties of the cyanobacterium Nodularia spumigena

Abstract. In the last century, an increasing number of extreme weather events have been experienced across the globe. These events have also been linked to changes in water quality, especially due to heavy rains, flooding, or droughts. In terms of blue economic activities, harmful algal bloom events can pose a major threat, especially when they become widespread and last for several days. We present and discuss advanced measurements of a bloom dominated by the cyanobacterium Nodularia spumigena conducted by hyperspectral optical technologies via experiments of opportunity. Absorption coefficients, absorbance and fluorescence were measured in the laboratory, and these data are available at https://doi.org/10.4121/21610995.v1 (Wollschläger et al., 2022), https://doi.org/10.4121/21822051.v1 (Miranda et al., 2023) and https://doi.org/10.4121/21904632.v1 (Miranda and Garaba, 2023). Data used to derive the above-water reflectance are available from https://doi.org/10.4121/21814977.v1 (Garaba, 2023) and https://doi.org/10.4121/21814773.v1 (Garaba and Albinus, 2023). Additionally, hyperspectral fluorescence measurements of the dissolved compounds in the water were carried out. These hyperspectral measurements were conducted over a wide spectrum (200–2500 nm). Diagnostic optical features were determined using robust statistical techniques. Water clarity was inferred from Secchi disc measurements (https://doi.org/10.1594/PANGAEA.951239, Garaba and Albinus, 2022). Identification of the cyanobacterium was completed via visual analysis under a microscope. Full sequences of the 16S rRNA and rbcL genes were obtained, revealing a very strong match to N. spumigena; these data are available via GenBank: https://www.ncbi.nlm.nih.gov/nuccore/OP918142/ (Garaba and Bonthond, 2022b) and https://www.ncbi.nlm.nih.gov/nuccore/OP925098 (Garaba and Bonthond, 2022a). The chlorophyll-a and phycocyanin levels determined are available from https://doi.org/10.4121/21792665.v1 (Rohde et al., 2023). Our experiments of opportunity echo the importance of sustainable, simplified, coordinated and continuous water quality monitoring as a way to thrive with respect to the targets set in the United Nations Sustainable Development Goals (e.g. 6, 11, 12 and 14) or the European Union Framework Directives (e.g. the Water Framework Directive and Marine Strategy Framework Directive).