Lake Chlorophyll-a Linked to Upstream Nutrients across the Conterminous United States DOI
Matthew Dietrich, Heather E. Golden, Jay R. Christensen

et al.

Environmental Science & Technology Letters, Journal Year: 2024, Volume and Issue: 11(12), P. 1406 - 1412

Published: Nov. 26, 2024

Chlorophyll-a (Chl-a) is a commonly used proxy for algal biomass within surface waters, which can be indicative of harmful blooms. Excess nutrients, such as nitrogen or phosphorus, promote Chl-a production, often leading to eutrophication. However, little research exists on river nutrients-to-downstream lake linkages at large watershed scales and across disparate climatic physiographic regions. We found significant positive relationship between measured total (TN) phosphorus (TP) concentrations in upstream rivers downstream lakes the scale (average area = 99.8 km2 [35.8–628.6 km2], n 254 watersheds) throughout conterminous United States (CONUS). Additionally, through spatial logistic regression models, we demonstrate that small number explanatory variables (2–3 per model) accurately predict (77%–86% accuracy, AUC 0.83–0.91) classifications high low riverine TN, TP, CONUS scale. The predictive included vegetation type, runoff, tile drainage, temperature, inputs. This work supports hypothesis supply nutrients enhance demonstrates power parsimonious models combined with autocorrelation nutrient CONUS.

Language: Английский

Fire Retardants Are an Overlooked Source of Phosphorus to Western US Ecosystems DOI
Leigh C. Moorhead, Michael J. Pennino, Robert D. Sabo

et al.

ACS ES&T Water, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

Citations

0
Machine learning-based estimation of chlorophyll-a in the Mississippi Sound using Landsat and ocean optics data DOI
Hafez Ahmad, Felix Jose, Padmanava Dash

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(7)

Published: March 18, 2025

Language: Английский

Citations

0
Estimating indicators of cyanobacterial harmful algal blooms in New York State DOI Creative Commons
Philip Savoy, Rebecca M. Gorney, Jennifer L. Graham

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 173, P. 113403 - 113403

Published: April 1, 2025

Language: Английский

Citations

0
Long-term successional dynamics and response strategies of harmful algal blooms to environmental changes in Tolo Harbour DOI

Jianhua Kang,

Xinyu Guo, Xuancheng Liu

et al.

Water Research, Journal Year: 2025, Volume and Issue: unknown, P. 123644 - 123644

Published: April 1, 2025

Language: Английский

Citations

0
Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants DOI
Sung-Hyun Yoon, Kuk-Hyun Ahn

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 979, P. 179481 - 179481

Published: April 24, 2025

Language: Английский

Citations

0
Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie DOI Creative Commons
Omer Mermer, İbrahim Demir

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(9), P. 4824 - 4824

Published: April 26, 2025

Harmful Algal Blooms (HABs), predominantly driven by cyanobacteria, pose significant risks to water quality, public health, and aquatic ecosystems. Lake Erie, particularly its western basin, has been severely impacted HABs, largely due nutrient pollution climatic changes. This study aims identify key physical, chemical, biological drivers influencing HABs using a multivariate regression analysis. Water quality data, collected from multiple monitoring stations in Erie 2013 2020, were analyzed develop predictive models for chlorophyll-a (Chl-a) total suspended solids (TSS). The correlation analysis revealed that particulate organic nitrogen, turbidity, carbon the most influential variables predicting Chl-a TSS concentrations. Two developed, achieving high accuracy with R2 values of 0.973 0.958 TSS. demonstrates robustness techniques identifying HAB drivers, providing framework applicable other systems. These findings will contribute better prediction management strategies, ultimately helping protect resources health.

Language: Английский

Citations

0
Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review DOI Creative Commons
Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik

et al.

Biology, Journal Year: 2025, Volume and Issue: 14(5), P. 520 - 520

Published: May 8, 2025

Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, conservation planning. This systematic review follows the PRISMA framework to analyze AI applications freshwater studies. Using structured literature search across Scopus, Web of Science, Google Scholar, we identified 312 relevant studies published between 2010 2024. categorizes into assessment, ecological risk evaluation, strategies. A bias assessment was conducted using QUADAS-2 RoB 2 frameworks, highlighting methodological challenges, such measurement inconsistencies model validation. The citation trends demonstrate exponential growth AI-driven with leading contributions from China, United States, India. Despite growing use this field, also reveals several persistent including limited data availability, regional imbalances, concerns related generalizability transparency. Our findings underscore AI’s potential revolutionizing but emphasize need for standardized methodologies, improved integration, interdisciplinary collaboration enhance insights efforts.

Language: Английский

Citations

0
Lake chlorophyll-a linked to upstream nutrients across the CONUS DOI Creative Commons
Matthew Dietrich, Heather E. Golden, Jay R. Christensen

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 12, 2024

Abstract Chlorophyll-a (Chl-a) is a commonly used proxy for algal biomass within surface waters, which can be indicative of harmful blooms. Excess nutrients, such as nitrogen or phosphorus, promote Chl-a production, often leading to eutrophication. However, little research exists on river nutrients-to-downstream lake linkages at large watershed scales and across disparate climatic physiographic regions. We found significant positive relationship between measured total (TN) phosphorous (TP) concentrations in upstream rivers downstream lakes the scale (average area = 99.8 km2 [35.8-628.6 km2], n 254 watersheds) throughout conterminous United States (CONUS). Additionally, through spatial logistic regression models, we demonstrate that small number explanatory variables (2–3 per model) accurately predict (77%-86% accuracy, AUC 0.83–0.91) classifications high low riverine TN, TP, CONUS scale. The predictive included vegetation type, runoff, tile drainage, temperature, inputs. This work supports hypothesis supply nutrients enhance demonstrates power parsimonious models combined with autocorrelation nutrient CONUS. Synopsis River are positively correlated chlorophyll-a both effectively predicted by incorporate autocorrelation.

Language: Английский

Citations

0
Lake Chlorophyll-a Linked to Upstream Nutrients across the Conterminous United States DOI
Matthew Dietrich, Heather E. Golden, Jay R. Christensen

et al.

Environmental Science & Technology Letters, Journal Year: 2024, Volume and Issue: 11(12), P. 1406 - 1412

Published: Nov. 26, 2024

Chlorophyll-a (Chl-a) is a commonly used proxy for algal biomass within surface waters, which can be indicative of harmful blooms. Excess nutrients, such as nitrogen or phosphorus, promote Chl-a production, often leading to eutrophication. However, little research exists on river nutrients-to-downstream lake linkages at large watershed scales and across disparate climatic physiographic regions. We found significant positive relationship between measured total (TN) phosphorus (TP) concentrations in upstream rivers downstream lakes the scale (average area = 99.8 km2 [35.8–628.6 km2], n 254 watersheds) throughout conterminous United States (CONUS). Additionally, through spatial logistic regression models, we demonstrate that small number explanatory variables (2–3 per model) accurately predict (77%–86% accuracy, AUC 0.83–0.91) classifications high low riverine TN, TP, CONUS scale. The predictive included vegetation type, runoff, tile drainage, temperature, inputs. This work supports hypothesis supply nutrients enhance demonstrates power parsimonious models combined with autocorrelation nutrient CONUS.

Language: Английский

Citations

0