{"doi":"10.1029/2020wr028773","title":"Hourly Prediction of Phytoplankton Biomass and Its Environmental Controls in Lowland Rivers","abstract":"<jats:title>Abstract</jats:title><jats:p>High‐resolution river modeling is valuable to study diurnal scale phytoplankton dynamics and understand biomass response to short‐term, rapid changes in its environmental controls. Based on theory contained in the Quality Evaluation and Simulation Tool for River‐systems model, a new river model is developed to simulate hourly scale phytoplankton growth and its environmental controls, thus allowing to study diurnal changes thereof. The model is implemented along a 62 km stretch in a lowland river, River Thames (England), using high‐frequency water quality measurements to simulate flow, water temperature, dissolved oxygen, nutrients, and phytoplankton concentrations for 2 years (2013–2014). The model satisfactorily simulates diurnal variability and transport of phytoplankton with Nash and Sutcliffe Efficiency (NSE) &gt; 0.7 at all calibration sites. Even without high‐frequency data inputs, the model performs satisfactorily with NSE &gt; 0.6. The model therefore can serve as a powerful tool both for predictive purposes and for hindcasting past conditions when hourly resolution water quality monitoring is unavailable. Model sensitivity analysis shows that the model with cool water diatoms as dominant species with an optimum growth temperature of 14°C performs the best for phytoplankton prediction. Phytoplankton blooms are mainly controlled by residence time, light and water temperature. Moreover, phytoplankton blooms develop within an optimum range of flow (21–63 m<jats:sup>3</jats:sup> s<jats:sup>−1</jats:sup>). Thus, lowering river residence time with short‐term high flow releases could help prevent major bloom developments. The hourly model improves biomass prediction and represents a step forward in high‐resolution phytoplankton modeling and consequently, bloom management in lowland river systems.</jats:p>","journal":"Water Resources Research","year":2021,"id":22617,"datarank":1.0629702150092966,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"self_citation_contribution":0.515098080672772,"citation_network_contribution":0.5478721343365245,"self_endowment_contribution":0.515098080672772,"citer_contribution":0.5478721343365245,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":24,"citers_with_citation_signal":20,"citers_with_endowment":20,"datacite_reuse_total":2,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":141606,"name":"Michael Hutchins","orcid":null,"position":1,"is_corresponding":false},{"id":141607,"name":"Lee Brown","orcid":null,"position":2,"is_corresponding":false},{"id":141608,"name":"Matthew Loewenthal","orcid":null,"position":3,"is_corresponding":false},{"id":141609,"name":"Peter Scarlett","orcid":null,"position":4,"is_corresponding":false},{"id":141610,"name":"Linda Armstrong","orcid":null,"position":5,"is_corresponding":false},{"id":131799,"name":"David Nicholls","orcid":null,"position":6,"is_corresponding":false},{"id":141611,"name":"Michael Bowes","orcid":null,"position":7,"is_corresponding":false},{"id":141612,"name":"François Edwards","orcid":null,"position":8,"is_corresponding":false},{"id":141605,"name":"Devanshi Pathak","orcid":"0000-0003-3290-5149","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"datacite_reuse_total":2,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21071399","pmcid":null,"openalex_id":"https://openalex.org/W3118961085","authors":[],"funders":[{"funder_name":"Horizon 2020 Framework Programme","grant_id":"765553","title":"A EUROpean training and research network for environmental FLOW management in river basins"}],"total_grants":1,"fwci":5.536,"citation_percentile":0.95870693,"influential_citations":0,"citation_trend":[{"year":2021,"count":2},{"year":2022,"count":4},{"year":2023,"count":4},{"year":2024,"count":10},{"year":2025,"count":9},{"year":2026,"count":1}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2020WR028773","host_type":"journal"},{"url":"https://eprints.whiterose.ac.uk/169957/13/2020WR028773.pdf","host_type":"GREEN"},{"url":"https://agupubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2020WR028773","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028773","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1029/2020WR028773","host_type":"publisher"},{"url":"https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028773","host_type":"publisher"},{"url":"https://doi.org/10.1029/2020wr028773","host_type":"journal"},{"url":"https://dx.doi.org/10.1029/2020wr028773","host_type":""},{"url":"http://nora.nerc.ac.uk/id/eprint/529399/","host_type":""}],"fields_of_study":["Fish Ecology and Management Studies","Hydrology and Watershed Management Studies","Soil and Water Nutrient Dynamics","Environmental Science","0207 environmental engineering","02 engineering and technology","01 natural sciences","0105 earth and related environmental sciences"],"mesh_terms":[],"keywords":["Phytoplankton","Environmental science","Water quality","Biomass (ecology)","Algal bloom","Residence time (fluid dynamics)","Hydrology (agriculture)","Nutrient","Oceanography","Ecology","Geology","river water quality","River Thames","phytoplankton modeling","flow regulation","high‐frequency data"],"sdg_mappings":[{"sdg_number":6,"sdg_label":"6. 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