Nutrient Retention in the Swedish Coastal Zone

Abstract

In this study, the average nutrient filter efficiency of the entire Swedish coastline is estimated to be about 54 and 70% for nitrogen and phosphorus, respectively. Hence, significantly less than half of the nutrient input from land (defined as river discharge and point sources) can be assumed to be exported from coastal waters to the open sea. However, some coastal areas retained more than 100% of the land load and thus, also filter the open Baltic Sea water. These areas with effective filtering of nutrients have low land load per unit coastal area. The filter efficiency was calculated from a 30-years model simulation (1985-2014) of water exchanges and nutrient cycling within the Swedish coastal zone. The average model skill was evaluated to be good or acceptable compared to observations. In addition to the entire Swedish coast, the retention of total nutrient loads in seven larger coastal sub-regions and selected key sites representing different coastal types was also estimated. The modeled long term nutrient retention was found to be associated with the physical characteristics of a water body, such as the surface area, but also the mean depth and residence time of water. In addition, high retention efficiency is associated with high ratio of sediment nutrient content to pelagic nutrient concentrations. On interannual timescales temporal changes in the coastal nutrient pool can have a large influence on perceived nutrient retention. At one site, the phosphorus filter efficiency was actually negative, i.e., the coastal zone transported more phosphorus to the open Baltic Sea than it received from land. The nutrient removal is most efficient close to land, where the area specific retention efficiency is the highest. The variability of both the filter and retention efficiency between coastal regions was found to be large, with a range of approximately 4-1,200% and 0.1-8.5%, respectively.

Nitrogen fixation estimates for the Baltic Sea indicate high rates for the previously overlooked Bothnian Sea

“…Data derived from the Swedish National Oceanographic Data Centre nitrogen derived from riverine runoff and from filamentous cyanobacteria are where it enters the water. For example, along the Baltic Sea coastline, biogeochemical processes remove nitrogen from riverine input by 50-100% (Edman et al 2018), and all of it therefore does not reach open Baltic Sea. While denitrification is a process capable of removing reactive nitrogen from a system, it is unlikely that denitrification is a significant sink of nitrogen in welloxygenated offshore water columns (Edman et al 2018).…”

Simulating Water Residence Time in the Coastal Ocean: A Global Perspective

“…This retention timescale constrains coast-ocean exchanges and allows for biogeochemical processes to impart their unique signatures on coastal environment. It has been shown that coastal retention may enhance productivity (Edman et al, 2018;Lohrenz et al, 1999), favor recruitment of larval fish (Mullaney & Suthers, 2013), constrain connectivity between coral reefs (Figueiredo et al, 2014), and facilitate development of harmful algal blooms (Pitcher et al, 2010) and coastal hypoxia Rabalais et al, 2014). Coastal retention and coast-ocean exchanges are also key to the role that the coastal ocean plays in the global carbon cycle.…”

Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

“…The sub-basins are identical to the defined national water body classification according to the EU WFD and are described by their hypsographical curve [44]. Recently, the SCM was used to simulate and describe coastal nutrient retention around Sweden [45], and had previously been used for a similar study in the Stockholm archipelago [46]. Both studies have shown that SCM skill to calculate the average state (average vertical profiles and mean seasonal variations) in coastal water bodies is usually good, or acceptable.…”

“…For this current study we apply the same cost function (for the same type of data distributions) as in the previous studies [45,46] to evaluate the proximity of the average model states to the average of in situ measurements at three locations in Bråviken. The proximity is evaluated by normalizing the bias between model (P) to observations (O) with the standard deviation of the observations, i.e.,…”

“…The presented study demonstrates that the requirements set by the EU WFD [17] and the Marine Strategy Framework Directive (MSFD) [52] in terms of 'good ecological/environmental status' can only be assessed adequately by combining various approaches, spanning from conventional monitoring by ship, to observations performed by ocean colour sensors from Space [20,36], and to data modelled by hydrological and oceanographic biogeochemical models [45,46]. However, extensive validation with in situ data is required in order to assure reliable quality of ocean colour and modelling data.…”