Abstract:This is of the same order of magnitude as the CO 2 emissions from land use change, or the carbon transport from continents to the ocean (Ciais et al., 2013), making CO 2 emissions from lakes important in the global carbon cycle. Lakes are concentrated in boreal regions, which contain roughly 30% of global lakes (Downing et al., 2006;Verpoorter et al., 2014), and together with the arctic region contribute 17% of global lake CO 2 emissions (Aufdenkampe et al., 2011). Potential climate change effects in boreal la… Show more
“…For example, CH 4 can exhibit midday maxima in concentration (Kim et al., 2015; Xing et al., 2004), while CO 2 levels often peak at night (Raymond et al., 2013; Wiik et al., 2018), and N 2 O has been observed to increase (Rosamond et al., 2011; Wu et al., 2018), decrease (Molina et al., 2021), and exhibit no change overnight (Baulch et al., 2012). These patterns are further complicated by potential diel variability in wind speed overnight (MacIntyre et al., 2021; Rudberg et al., 2021). Here, we chose to calculate CO 2 ‐eq flux using observed daytime values and extrapolating over the full 24‐hr period, because an earlier investigation of these regional ponds did not reveal any evidence of consistent diel variability of GHG fluxes (Jensen et al., 2022).…”
Small inland waterbodies are well-known hotspots of greenhouse gas (GHG) emissions (Cole et al., 2007;Tranvik et al., 2009) owing to their cumulative abundance in many regions (Downing et al., 2006) and because they often release carbon dioxide (CO 2 ) and methane (CH 4 ) at higher rates than larger inland waters (Downing, 2010). However, GHG concentrations are extremely variable, both spatially and temporally in these small surface waters (
“…For example, CH 4 can exhibit midday maxima in concentration (Kim et al., 2015; Xing et al., 2004), while CO 2 levels often peak at night (Raymond et al., 2013; Wiik et al., 2018), and N 2 O has been observed to increase (Rosamond et al., 2011; Wu et al., 2018), decrease (Molina et al., 2021), and exhibit no change overnight (Baulch et al., 2012). These patterns are further complicated by potential diel variability in wind speed overnight (MacIntyre et al., 2021; Rudberg et al., 2021). Here, we chose to calculate CO 2 ‐eq flux using observed daytime values and extrapolating over the full 24‐hr period, because an earlier investigation of these regional ponds did not reveal any evidence of consistent diel variability of GHG fluxes (Jensen et al., 2022).…”
Small inland waterbodies are well-known hotspots of greenhouse gas (GHG) emissions (Cole et al., 2007;Tranvik et al., 2009) owing to their cumulative abundance in many regions (Downing et al., 2006) and because they often release carbon dioxide (CO 2 ) and methane (CH 4 ) at higher rates than larger inland waters (Downing, 2010). However, GHG concentrations are extremely variable, both spatially and temporally in these small surface waters (
“…As previous reported the GHG emissions from freshwater bodies may have strong variation over a day (Rudberg et al, 2021; Sieczko et al, 2020), thus a selected sampling site was employed for a 24‐h observations for GHG fluxes. Figure 4 shows a clear pattern that the variation of CH 4 emissions were consistent with temperature changes over 24‐h observations.…”
In the modern era, urban freshwater bodies play a significant role in global carbon (C) budgeting, therefore, impacting climate change under rising global mean temperature. However, the trend, magnitude and drivers of greenhouse gas (GHG) emissions in these urban freshwater bodies of China remain uncertain. This study investigated temporal changes in GHG emissions in urban water bodies, including artificial lakes, reservoirs, aquaculture ponds and rivers, within a year in Nanjing city in the areas of the Yangtze River delta of China. In addition, meteorological and hydrochemical parameters were measured to elucidate the key drivers of GHG emissions. The results showed that the average annual flux of carbon dioxide (CO2) and methane (CH4) in aquaculture ponds was estimated to be 1355.6 mg CO2 m−2 day−1 and 116.6 mg CH4 m−2 day−1, followed by that of artificial lakes were 1172.2 mg CO2 m−2 day−1 and 44.1 mg CH4 m−2 day−1, rivers reached 775.9 mg CO2 m−2 day−1 and 23.9 mg CH4 m−2 day−1 and reservoirs were 170.1 mg CO2 m−2 day−1 and 7.2 mg CH4 m−2 day−1, respectively. The results further suggest that although artificial lakes and aquaculture ponds occupied only 23% of the cumulative area under lakes and ponds in the Yangtze River delta, contribute approximately 43%, about 27.5 Gg C, of total fluxes. Furthermore, high concentrations of dissolved organic carbon (DOC) and low dissolved oxygen (DO) coincided with the high GHG emissions. The study suggests that DO, DOC, temperature and wind speed are the key factors impacting the potential of GHG emissions in urban freshwater ecosystems. Strategic mitigation measures in the vicinity of the urban freshwater bodies could efficiently reduce carbon emissions in the future.
“…It is possible that wind suppresses the phytoplankton signal on lake pCO 2 , because CO 2 at the lake surface, where phytoplankton are photosynthesizing and, thus, consuming CO 2 , is mixed with CO 2 from waters from below. Wind exposure may also explain why lake pCO 2 does not increase in autumn after mixing starts in this lake, even though this is typically observed in eutrophic lakes when deep-water, CO 2 water mixes with surface oxygen-rich water [30].…”
Section: Discussionmentioning
confidence: 96%
“…During migration, it easy to underestimate G. semen densities by sampling too early or too late during the day. It is also known that the carbon ux has high diel and seasonal variability [30,34], with diel variations being especially pronounced during mixing events [30]. Thus, it is likely that the pCO 2 measurements at the surface of the lakes caused an under-or overestimation of pCO 2 for our sampling dates due to the time of sampling.…”
Lakes located in the boreal region are generally supersaturated with carbon dioxide (CO2), which emerges from inflowing inorganic carbon from the surrounding watershed and from mineralization of allochthonous organic carbon. While these CO2 sources gained a lot of attention, processes that reduce the amount of CO2 have been less studied. We therefore examined the CO2 reduction capacity during times of phytoplankton blooms. We investigated partial pressure of CO2 (pCO2) at times of blooms dominated by cyanobacteria (lake Erken, Sweden) or dominated by the nuisance alga Gonyostomum semen (lake Erssjön, Sweden) during two years. Our results showed that pCO2 and phytoplankton densities remained unrelated in the two lakes even during blooms. We suggest that physical factors, such as wind-induced water column mixing and import of inorganic carbon via inflowing waters suppressed the phytoplankton signal on pCO2. These results advance our understanding of carbon cycling in lakes and highlight the importance of detailed lake studies for more precise estimates of local, regional and global carbon budgets.
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