Modeling of the large-scale nutrient biogeochemical cycles in Lake Onego

Abstract

Abstract. Despite a long history of research, there is almost no information regarding the major biogeochemical fluxes that could characterize the past and present state of the European Lake Onego ecosystem and be used for reliable prognostic estimates of its future. To enable such capacity, we adapted and implemented a three-dimensional coupled hydrodynamical biogeochemical model of the nutrient cycles in Lake Onego. The model was used to reconstruct three decades of Lake Onego ecosystem dynamics with daily resolution on a 2 × 2 km grid. A comparison of available information from Lake Onego and other large boreal lakes proves that this hindcast is plausible enough to be used as a form of reanalysis. As new regional phenological knowledge, the reanalysis quantifies that the spring phytoplankton bloom, previously overlooked, reaches a maximum of 500 ± 128 mg C m−2 d−1 in May, contributes to approximately half of the lake’s annual primary production of 17.0–20.6 g C m−2 yr−1, and is triggered by increasing light availability rather than by an insignificant rise in water temperature. Coherent nutrient budgets provide reliable estimates of phosphorus and nitrogen residence times of 47 and 17 years, respectively. The shorter nitrogen residence time is explained by sediment denitrification, which in Lake Onego removes over 90 % of the bioavailable nitrogen input, but is often ignored in studies of other large lakes. This model can be used for long-term projections as soon as the corresponding scenarios of climate change and socio-economic development become available for north-western Russia.

hydrographr: An R package for scalable hydrographic data processing

“…In this regard, we expect the package to be equally useful beyond ecological studies in the freshwater realm. Studies targeting large spatial extents and simultaneously high spatial resolution, for instance to study carbon (Ludwig et al., 2023; Raymond et al., 2013) or nutrient cycles in inland waters (Savchuk et al., 2021), can capitalise on efficient network and basin data processing. Likewise, even at smaller spatial extents, but temporally high‐resolved, for example water quality/quantity data at specific intervals over long time frames, may result in equal amounts of data that require efficient processing tools.…”