Shorter blooms expected with longer warm periods under climate change: an example from a shallow meso-eutrophic Mediterranean lake

• Published in: Hydrobiologia
• Main Authors: Gary Free, Mariano Bresciani, Monica Pinardi, Steef Peters, Marnix Laanen, Rosalba Padula, Alessandra Cingolani, Fedra Charavgis, Claudia Giardino
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2022
• Subjects: Energy Research; EU-CONEXUS; H2020; EOMORES; Coordination and support action; Innovation action; EC; Rural Digital Europe; European Commission; projects; Aquatic Science; European Space Agency; Horizon 2020 Framework Programme; North Atlantic; North Atlantic oscillation
• Online Access: https://www.openaccessrepository.it/record/80635; https://doi.org/10.1007/s10750-021-04773-w

AbstractSatellite data from the Climate Change Initiative (CCI) lakes project were used to examine the influence of climate on chlorophyll-a (Chl-a). Nonparametric multiplicative regression and machine learning were used to explain Chl-a concentration trend and dynamics. The main parameters of importance were seasonality, interannual variation, lake level, water temperature, the North Atlantic Oscillation, and antecedent rainfall. No evidence was found for an earlier onset of the summer phytoplankton bloom related to the earlier onset of warmer temperatures. Instead, a curvilinear relationship between Chl-a and the temperature length of season above 20°C (LOS) was found with longer periods of warmer temperature leading to blooms of shorter duration. We suggest that a longer period of warmer temperatures in the summer may result in earlier uptake of nutrients or increased calcite precipitation resulting in a shortening of the duration of phytoplankton blooms. The current scenario of increasing LOS of temperature with climate change may lead to an alteration of phytoplankton phenological cycles resulting in blooms of shorter duration in lakes where nutrients become limiting. Satellite-derived information on lake temperature and Chl-a concentration proved essential in detecting trends at appropriate resolution over time.