These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
2. Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability. Woelmer WM; Thomas RQ; Lofton ME; McClure RP; Wander HL; Carey CC Ecol Appl; 2022 Oct; 32(7):e2642. PubMed ID: 35470923 [TBL] [Abstract][Full Text] [Related]
3. Influence of cyanobacterial blooms and environmental variation on zooplankton and eukaryotic phytoplankton in a large, shallow, eutrophic lake in China. Zhao K; Wang L; You Q; Pan Y; Liu T; Zhou Y; Zhang J; Pang W; Wang Q Sci Total Environ; 2021 Jun; 773():145421. PubMed ID: 33582356 [TBL] [Abstract][Full Text] [Related]
4. Progress and opportunities in advancing near-term forecasting of freshwater quality. Lofton ME; Howard DW; Thomas RQ; Carey CC Glob Chang Biol; 2023 Apr; 29(7):1691-1714. PubMed ID: 36622168 [TBL] [Abstract][Full Text] [Related]
5. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs. Schaeffer BA; Reynolds N; Ferriby H; Salls W; Smith D; Johnston JM; Myer M J Environ Manage; 2024 Jan; 349():119518. PubMed ID: 37944321 [TBL] [Abstract][Full Text] [Related]
6. Hydrologic and nutrient-driven regime shifts of cyanobacterial and eukaryotic algal communities in a large shallow lake: Evidence from empirical state indicator and ecological network analyses. Zhang H; Huo S; Wang R; Xiao Z; Li X; Wu F Sci Total Environ; 2021 Aug; 783():147059. PubMed ID: 33865117 [TBL] [Abstract][Full Text] [Related]
7. Using Bayesian hierarchical modelling to capture cyanobacteria dynamics in Northern European lakes. Mellios NK; Moe SJ; Laspidou C Water Res; 2020 Nov; 186():116356. PubMed ID: 32889364 [TBL] [Abstract][Full Text] [Related]
8. Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie. Scavia D; Wang YC; Obenour DR Sci Total Environ; 2023 Jan; 856(Pt 1):158959. PubMed ID: 36155036 [TBL] [Abstract][Full Text] [Related]
9. Increased adoption of best practices in ecological forecasting enables comparisons of forecastability. Lewis ASL; Woelmer WM; Wander HL; Howard DW; Smith JW; McClure RP; Lofton ME; Hammond NW; Corrigan RS; Thomas RQ; Carey CC Ecol Appl; 2022 Mar; 32(2):e2500. PubMed ID: 34800082 [TBL] [Abstract][Full Text] [Related]
10. Accuracy of data buoys for measurement of cyanobacteria, chlorophyll, and turbidity in a large lake (Lake Erie, North America): implications for estimation of cyanobacterial bloom parameters from water quality sonde measurements. Chaffin JD; Kane DD; Stanislawczyk K; Parker EM Environ Sci Pollut Res Int; 2018 Sep; 25(25):25175-25189. PubMed ID: 29943249 [TBL] [Abstract][Full Text] [Related]
11. Development and evaluation of a real-time forecasting framework for daily water quality forecasts for Lake Chaohu to Lead time of six days. Peng Z; Hu W; Liu G; Zhang H; Gao R; Wei W Sci Total Environ; 2019 Oct; 687():218-231. PubMed ID: 31207512 [TBL] [Abstract][Full Text] [Related]
12. [Analysis of Influencing Factors of Chlorophyll-a in Lake Taihu Based on Bayesian Network]. Liu J; He YC; Deng JM; Tang XM Huan Jing Ke Xue; 2023 May; 44(5):2592-2600. PubMed ID: 37177933 [TBL] [Abstract][Full Text] [Related]
13. Cyanobacterial blooms: statistical models describing risk factors for national-scale lake assessment and lake management. Carvalho L; Miller nee Ferguson CA; Scott EM; Codd GA; Davies PS; Tyler AN Sci Total Environ; 2011 Nov; 409(24):5353-8. PubMed ID: 21975001 [TBL] [Abstract][Full Text] [Related]
14. Ecological forecasting of tree growth: Regional fusion of tree-ring and forest inventory data to quantify drivers and characterize uncertainty. Heilman KA; Dietze MC; Arizpe AA; Aragon J; Gray A; Shaw JD; Finley AO; Klesse S; DeRose RJ; Evans MEK Glob Chang Biol; 2022 Apr; 28(7):2442-2460. PubMed ID: 35023229 [TBL] [Abstract][Full Text] [Related]
15. Response of the photosynthetic activity and biomass of the phytoplankton community to increasing nutrients during cyanobacterial blooms in Meiliang Bay, Lake Taihu. Wu P; Lu Y; Lu Y; Dai J; Huang T Water Environ Res; 2020 Jan; 92(1):138-148. PubMed ID: 31486194 [TBL] [Abstract][Full Text] [Related]
16. High-resolution temporal detection of cyanobacterial blooms in a deep and oligotrophic lake by high-frequency buoy data. Zhang M; Zhang Y; Deng J; Liu M; Zhou Y; Zhang Y; Shi K; Jiang C Environ Res; 2022 Jan; 203():111848. PubMed ID: 34390714 [TBL] [Abstract][Full Text] [Related]
17. Sporadic diurnal fluctuations of cyanobacterial populations in oligotrophic temperate systems can prevent accurate characterization of change and risk in aquatic systems. Cameron ES; Krishna A; Emelko MB; Müller KM Water Res; 2024 Mar; 252():121199. PubMed ID: 38330712 [TBL] [Abstract][Full Text] [Related]
18. Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu (China). Qin B; Li W; Zhu G; Zhang Y; Wu T; Gao G J Hazard Mater; 2015 Apr; 287():356-63. PubMed ID: 25679801 [TBL] [Abstract][Full Text] [Related]
19. Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva. Derot J; Yajima H; Jacquet S Harmful Algae; 2020 Nov; 99():101906. PubMed ID: 33218452 [TBL] [Abstract][Full Text] [Related]
20. Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations. Wang S; Zhang X; Chen N; Wang W J Environ Manage; 2022 May; 310():114782. PubMed ID: 35247688 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]