Bibliometric network analysis of scientific research on early warning signals for cyanobacterial blooms in lakes and rivers

Harmful cyanobacterial blooms (HCBs) present a major risk to inland waters; therefore, various monitoring and management frameworks have been implemented to protect water quality, aquatic organisms, and humans from their negative impacts. Enabling proactive rather than reactive management, early warning systems within the lead time of HCBs at timescales ranging from hours to days is necessary to provide water managers with timely, evidence-based information for decision-making. To provide a state-of-the-art early warning system for HCBs, this study systematically reviewed scientific publications indexed in the Web of Science through bibliometric approaches, investigating current trends and developments. By focusing on the literature addressing the period preceding HCBs, a quantitative network analysis identified key indicators, state variables, and forecast horizons. Consequently, 116 documents related to eutrophic lakes and reservoirs in temperate, Mediterranean, and subtropical climates were analyzed. The frequently used HCB predictors in these studies were chlorophyll-a (chla) concentration and water temperature, while the commonly targeted outputs were chla and cyanobacterial cell density. Co-occurrence network analysis of the keywords addressed six clusters as the main research fields: molecular monitoring, remote sensing, in situ monitoring, resilience indicator utility, and inferential and deterministic modeling. The keywords were similarly identified by the network in the selected publications; however, specific terms associated with molecular identification, taste, and odor compounds were not observed. The results suggest that considerable progress in the early warning of HCBs requires enhancing interdisciplinary research to integrate the most relevant monitoring technologies, environmental indicators, and ecological knowledge about HCBs.