Carbon dioxide emissions and sediment organic carbon burials across a gradient of trophic state in eleven New Zealand lakes

Lakes are known to be important to the global carbon balance as they are both CO₂ sources to the atmosphere and also accumulate large amounts of carbon in their sediment. CO₂ flux dynamics across the air–water interface in 11 lakes of varying trophic state in the Rotorua region, New Zealand, derived from measured alkalinity, pH and wind speed at given temperature, showed that lakes may shift from being atmospheric CO₂ sources to sinks due to seasonal changes in phytoplankton productivity and lake mixing dynamics. Decreases in trophic state (i.e. improved water quality) in some of the lakes over the eight-year monitoring period were associated with increased surface water CO₂ concentrations and, as a consequence, increased CO₂ flux to the atmosphere. Organic carbon content analysis of the bottom sediments revealed that lakes with high phytoplankton productivity, indicated by high chlorophyll a biomass, generally had high rates of carbon deposition to the sediments, but not all deposited carbon was permanently buried. Remineralization of the organic carbon accrued in productive lakes may potentially generate CO₂, as well as CH₄, which promotes these lakes to act as greenhouse gas emitters

The importance of lake-specific characteristics for water quality across the continental United States

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency’s 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to- volume ratio) were important for explaining water quality (54–60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28–39% variance explained). Basin-scale land use and land cover explained between 45–62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers

Consequences of gas flux model choice on the interpretation of metabolic balance across 15 lakes

Ecosystem metabolism and the contribution of carbon dioxide from lakes to the atmosphere can be estimated from free-water gas measurements through the use of mass balance models, which rely on a gas transfer coefficient (k) to model gas exchange with the atmosphere. Theoretical and empirically based models of k range in complexity from wind-driven power functions to complex surface renewal models; however, model choice is rarely considered in most studies of lake metabolism. This study used high-frequency data from 15 lakes provided by the Global Lake Ecological Observatory Network (GLEON) to study how model choice of k influenced estimates of lake metabolism and gas exchange with the atmosphere. We tested 6 models of k on lakes chosen to span broad gradients in surface area and trophic states; a metabolism model was then fit to all 6 outputs of k data. We found that hourly values for k were substantially different between models and, at an annual scale, resulted in significantly different estimates of lake metabolism and gas exchange with the atmosphere

Appendix A. Model and predictor variable descriptions.

Model and predictor variable descriptions