Hydroclimate volatility on a warming Earth

Nature Reviews Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1), P. 35 - 50

Published: Jan. 9, 2025

Language: Английский

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Integrating Remote Sensing and Machine Learning for Dynamic Monitoring of Eutrophication in River Systems: A Case Study of Barato River, Japan

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 89 - 89

Published: Jan. 1, 2025

Rivers play a crucial role in nutrient cycling, yet are increasingly affected by eutrophication due to anthropogenic activities. This study focuses on the Barato River Hokkaido, Japan, employing an integrated approach of field measurements and Sentinel-2 satellite remote sensing monitor as river experiencing huge sewage effluents. Key parameters such chlorophyll-a (Chla), dissolved inorganic nitrogen (DIN), phosphorus (DIP), Secchi Disk Depth (SDD) were analyzed. The developed empirical models showed strong predictive capability for water quality, particularly Chla (R2 = 0.87), DIP 0.61), SDD 0.82). Seasonal analysis indicated peak concentrations October, reaching up 92.4 μg/L, alongside significant decreases DIN DIP, suggesting high phytoplankton activity. Advanced machine learning models, specifically back propagation neural networks, improved prediction accuracy with R2 values 0.90 0.83 DIN. Temporal analyses from 2018 2022 consistently revealed River’s eutrophic state, severe occurring 33% year moderate over 50%, emphasizing ongoing imbalance. correlation between highlights main driver eutrophication. These findings demonstrate efficacy integrating dynamic monitoring eutrophication, providing critical insights management quality improvement.

Language: Английский

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Increasing exposure to global climate change and hopes for the era of climate adaptation: An aquatic perspective

AMBIO, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Language: Английский

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Machine Learning-Based Early Warning of Algal Blooms: A Case Study of Key Environmental Factors in the Anzhaoxin River Basin

Water, Journal Year: 2025, Volume and Issue: 17(5), P. 725 - 725

Published: March 1, 2025

Algal blooms are a major risk to aquatic ecosystem health and potable water safety. Traditional statistical models often fail accurately predict algal bloom dynamics due their complexity. Machine learning, adept at managing high-dimensional non-linear data, provides superior predictive approach this challenge. In study, we employed support vector machine (SVM), random forest (RF), backpropagation neural network (BPNN) the severity of in Anzhaoxin River Basin based on an density-based grading standard. The SVM model demonstrated highest accuracy with training test set accuracies 0.96 0.92, highlighting its superiority small-sample learning. Shapley Additive Explanations (SHAP) technique was utilized evaluate contribution environmental variables various models. results show that TP is most significant factor affecting outbreak River, phosphorus management strategy more suitable for artificial body northeast China. This study contributes exploring potential application learning diagnosing predicting riverine ecological issues, providing valuable insights protection ecosystems Basin.

Language: Английский

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Water Clarity Assessment Through Satellite Imagery and Machine Learning

Water, Journal Year: 2025, Volume and Issue: 17(2), P. 253 - 253

Published: Jan. 17, 2025

Leveraging satellite monitoring and machine learning (ML) techniques for water clarity assessment addresses the critical need sustainable management. This study aims to assess by predicting Secchi disk depth (SDD) using images ML techniques. The primary methods involve data preparation SSD inference. During preparation, AquaSat samples, originally from L1TP collection, were updated with Landsat 8 satellite’s latest postprocessing, L2SP, which includes atmospheric corrections, resulting in 33,261 multispectral observations corresponding measurements. For inferring SSD, regressors such as SVR, NN, XGB, along an ensemble of them, trained. demonstrated performance average determination coefficient R2 around 0.76 a standard deviation 0.03. Field validation achieved 0.80. Furthermore, we show that trained imagery result favorable respect their counterparts on L2SP collection. document contributes transition semi-analytical data-driven research, bodies through imagery.

Language: Английский

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Research progress on fungi and their spore inactivation in different water bodies

Desalination and Water Treatment, Journal Year: 2025, Volume and Issue: 321, P. 101007 - 101007

Published: Jan. 1, 2025

Language: Английский

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Impact of water quality parameters on harmful algal bloom mitigation and phosphorus removal by lab-synthesized γFe2O3/TiO2 magnetic photocatalysts

Algal Research, Journal Year: 2025, Volume and Issue: 86, P. 103932 - 103932

Published: Jan. 27, 2025

Language: Английский

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Nutrient limitation and seasonality associated with phytoplankton communities and cyanotoxin production in a large, hypereutrophic lake

Harmful Algae, Journal Year: 2025, Volume and Issue: 143, P. 102809 - 102809

Published: Feb. 7, 2025

Though freshwater harmful algal blooms have been described and studied for decades, several important dynamics remain uncertain, including the relationships among nutrient concentrations, phytoplankton growth, cyanotoxin production. To identify when where nutrients limit phytoplankton, cyanobacteria, cyanotoxins, we conducted in situ bioassay studies. We added nitrogen (N), phosphorus (P), or N + P across various seasons water collected from three locations Utah Lake, one of largest lakes western U.S. This shallow, hypereutrophic lake provides a powerful testbed quantifying nutrient-growth-toxin interactions. assessed range parameters over time, photopigment abundance (cell counts), concentrations. Despite high background concentrations water, composition were strongly affected by addition. Phosphorus limitation was more common spring, with becoming fall. Nutrient additions positively associated cyanobacteria (Microcystis, Aphanocapsa, Dolichospermum, Merismopedia, Aphanizomenon spp.), eukaryotes (Aulacoseira, Desmodesmus two taxonomical categories (i.e., unicellular colonial green algae). When detected, anatoxin-a negatively Microcystis spp. However, overall not cyanobacterial cell density but varied seasonally. These findings highlight importance considering seasonal availability provide insights into specific targets, species, cyanotoxins that play significant role health management similar eutrophic environments around world.

Language: Английский

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The Need to Increase Strain-Specific DNA Information from the Invasive Cyanobacteria Sphaerospermopsis aphanizomenoides and Cuspidothrix issatschenkoi

Water, Journal Year: 2025, Volume and Issue: 17(4), P. 579 - 579

Published: Feb. 17, 2025

Climate change is promoting the occurrence of Harmful Cyanobacterial Blooms (HCBs) across freshwaters, posing serious risks for ecosystems and human health. Under these warmer conditions, particularly blooms invasive Aphanizomenon-like species such as Cuspidothrix issatschenkoi Sphaerospermopsis aphanizomenoides (previously known Aphanizomenon Aphanizomenon/Anabaena aphanizomenoides, respectively) have been reported to spread higher latitudes, leading increased toxic risks. Anabaena genera undergone several taxonomical revisions in recent years due their morphological ambiguity, also corroborated by a high phylogenetic diversity. Furthermore, there phenotypic genotypic variability within each one species, diverse physiological ecological traits. Therefore, DNA-based information crucial not only overcome possible misidentifications, but provide at strain level. However, still lack geographically dispersed strains with available nucleotide sequences databases, limiting deeper studies better understand ecology trend. This review aimed compile discuss geographical distribution found NCBI database make some recommendations on need increase numbers under exponential inputs from DNA-metabarcoding. The integration water quality monitoring programmes identify reoccurring bloom-forming physiology ecology, ultimately effective forecast, mitigation potential massive growth target freshwater bodies.

Language: Английский

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A 150-year river water quality record shows reductions in phosphorus loads but not in algal growth potential

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: Feb. 24, 2025

Language: Английский

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