Freshwater cyanobacterial blooms have become ubiquitous, posing major threats to ecological and public health. Decades of research have focused on understanding drivers of these blooms with a primary focus on eutrophic systems; however, cyanobacterial blooms also occur in oligotrophic systems, but have received far less attention, resulting in a gap in our understanding of cyanobacterial blooms overall. In this review, we explore evidence of cyanobacterial blooms in oligotrophic freshwater systems and provide explanations for those occurrences. We show that through their unique physiological adaptations, cyanobacteria are able to thrive under a wide range of environmental conditions, including low‐nutrient waterbodies. We contend that to fully understand cyanobacterial blooms, and thereby mitigate and manage them, we must expand our inquiries to consider systems along the trophic gradient, and not solely focus on eutrophic systems, thus shifting the high‐nutrient paradigm to a trophic‐gradient paradigm.

Anthropogenic activities such as intensive agriculture and waste water discharge deteriorate the quality of water resources. More specifically, anthropogenic sources increase the aquatic concentration and fluxes of nutrients like nitrogen, phosphorus and organic carbon, leading to eutrophication in rivers, lakes and coastal waters (Carpenter et al., 2011;Schlesinger, 2009). This can pose a threat to water security and downstream aquatic ecosystem health and functioning (Foley et al., 2005). For effective catchment-scale water quality management, knowledge on sources, pathways and reaction processes of critical constituents is needed. Reactive transport at the catchment scale, is however, complex and tends to span a large range of spatial and temporal scales (Gall et al., 2013;Kirchner, 2003;Sivapalan, 2006). This often hinders establishing a unique cause-effect relationship.One way of approaching this complexity involves the analysis of the integrated response of concentration (C) of a constituent and discharge (Q) at a given point in the stream to identify underlying processes and their hierarchy (Basu et al., 2011;Godsey et al., 2009;Musolff et al., 2015). More specifically, characterizing the relationship between concentration and discharge proved valuable to link temporal patterns in the data to dominant processes at the catchment scale (Sivapalan, 2006). This link may however not always be fully

Freshwater ecosystems including lakes and reservoirs are hot spots for retention of excess nitrogen (N) from anthropogenic sources, providing valuable ecological services for downstream and coastal ecosystems. Despite previous investigations, current quantitative understanding on the influential factors and underlying mechanisms of N retention in lentic freshwater systems is insufficient due to data paucity and limitation of modeling techniques. Our ability to reliably predict N retention for these systems therefore remains uncertain. Emerging high frequency monitoring techniques and well-developed ecosystem modeling shed light on this issue. In the present study, we explored the retention of NO3-N during a five-year period (2013-2017) in both annual and weekly scales in a highly flushed reservoir in Germany. We found that annual-averaged NO3-N retention efficiency could be up to 17% with an overall retention efficiency of ~4% in such a system characterized by a water residence time (WRT) of ~4 days. On the weekly scale, the reservoir displayed negative retention in winter (i.e. a source of NO3-N) and high positive retention in summer (i.e. a sink for NO3-N). We further identified the critical role of Chl-a concentration together with the well-recognized effects from WRT in dictating NO3-N retention efficiency, implying the significance of biological processes including phytoplankton dynamics in driving NO3-N retention. Furthermore, our modeling approach showed that an established process-based ecosystem model (PCLake) accounted for 58.0% of the variance in NO3-N retention efficiency, whereas statistical models obtained a lower value (40.5%). This finding exemplified the superior predictive power of process-based models over statistical models whenever ecological processes were at play. Overall, our study highlights the importance of high frequency data in providing new insights into evaluating and modeling N retention in reservoirs.