Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at Diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of Diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface Diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer Diel ranges in their near-surface temperatures of between 4 and 7°C. Large Diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored

Temporal development of dissolved organic carbon (DOC), absorbance (a₄₂₀), iron (Fe) and reactive silica (Si) in Swedish freshwaters and changes in long-term annual precipitation across Sweden since 1996.

<p>The DOC, a₄₂₀, Fe and Si data (panels A–D) are based on annual mean values from 66 lakes, streams and river mouths for which complete monthly time series were available. Thus, for each year, 66 data points have been used for the percentile calculations. For panel E, 10-year running means of data from entire Sweden have been used (see methods).</p

Decreasing iron (Fe), dissolved organic carbon (DOC) and absorbance (a₄₂₀) with increasing percentage of lake surface area in the catchment area (% Water).

<p>The figure shows the predicted values of Fe, DOC and a₄₂₀ from the simple exponential decay functions along the % Water gradient presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088104#pone-0088104-g003" target="_blank">Fig. 3</a>. Fe and a₄₂₀ decline equally fast with increasing % Water. The Fe and a₄₂₀ decline is substantially faster than the decline of DOC. % Water can be seen as a proxy for water retention in the landscape (see methods).</p

Relationships between iron (Fe) and the carbon specific absorbance (a₄₂₀/DOC) based on all available data from Swedish lakes, streams and river mouths (panel A) and confirmed by data from Canadian lakes (panel B).

<p>In panel B we predicted a₄₂₀/DOC by using the regression equation of panel A and obtained the regression line which is shown. Note the different scales between panel A and B.</p

Iron (Fe), dissolved organic carbon (DOC), absorbance (a₄₂₀), Fe/DOC and a₄₂₀/DOC ratios in relation to the percentage of lake surface area in the catchment area (% Water).

<p>For the figure, site-specific long-term median data of 5837 different lakes and streams were used. Taking the median of each of the 11% Water categories and applying a simple exponential decay along the % Water gradient we received highly significant results (<i>P</i><0.0001, <i>n</i> = 11; <i>R²</i> = 0.78 for Fe/DOC in panel A, <i>R²</i> = 0.84 for a₄₂₀/DOC in panel B, <i>R²</i> = 0.95 for Fe in panel C, <i>R²</i> = 0.89 for a₄₂₀ in panel D and <i>R²</i> = 0.88 for DOC in panel E).</p

Prediction of absorbance (a₄₂₀) by dissolved organic carbon (DOC), iron (Fe) and particulate matter (particles) for 46787 Swedish water samples.

<p>Particulate matter was assessed by the absorbance ratio between unfiltered and filtered water (see methods). 92% of the a₄₂₀ variations could be explained by the simple standard least squares model (panel A; a₄₂₀ = e^{(0.73+0.76·ln<i>DOC</i>+0.38·ln<i>Fe</i>−0.83·ln<i>Particles</i>)}) where all three input variables made a significant contribution to the model performance, here shown by model leverage plots (panels B–D). Removing the input variable particles from the model, the model performance decreased to <i>R</i>² = 0.85, <i>P</i><0.0001, <i>n</i> = 46787. Using only DOC as input variable the model performance was <i>R</i>² = 0.73, <i>P</i><0.0001, <i>n</i> = 46787.</p

Fate of iron (Fe), dissolved organic carbon (DOC) and absorbance (a₄₂₀) along the aquatic continuum under normal wet conditions (panel A) and in a wetter climate (panel B).

<p>When water travels from headwaters to river mouths and passes lakes Fe, DOC and a₄₂₀ all decline (compare <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088104#pone-0088104-g003" target="_blank">Fig. 3</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088104#pone-0088104-g004" target="_blank">Fig. 4</a>). It is suggested that a concomitant Fe, DOC and a₄₂₀ decline in surface waters of lakes is a result of Fe-OC complexes that can flocculate and reach bottom waters and sediments (panel A). In a wetter climate with a consequent faster flushing of waters through lakes the settling of Fe-OC complexes towards bottom waters and sediments becomes less efficient and Fe-OC complexes reach downstream waters, where they cause strong declines in a₄₂₀ (panel B). The conceptual figure assumes that Fe-OC complexes mainly originate from soils.</p

Fe Trends

The data file (FeTrends.xlxs) contains data for the 340 water bodies (headwater streams, higher-order streams, lakes and river mouths) included in the Fe trends study (Björnerås et al. 2017). The data has been collected from various monitoring programs and initiatives in 10 countries and include time series of iron (Fe), organic carbon (OC), silica (Si) and sulfate (SO4) concentrations in surface waters spanning from 1990 to 2013. Catchment variables, such as catchment size and land-use, are also included in the dataset, as well as climate data (precipitation and air temperature)

Temporal variability in near-surface lake water temperature.

<p>(a) Seasonal variability in the diel temperature range for 96 Northern Hemisphere lakes with 95% confidence intervals (note that not all lakes had data for the whole year). (b) Individually normalized (zero-mean) summer average diel cycle for the lakes that had the highest (red) and lowest (blue) 10% of diel temperature ranges measured. The bold lines represent the mean diel cycle for the 10% considered and the horizontal black line indicates zero. For clarity, we excluded Jekl Bog, which had the highest diel cycle, from this figure. (c) Example of hourly-resolution near-surface lake water temperature variation at Jekl Bog (surface area 2.5 x 10³ m², red), and Sparkling Lake (surface area 6.2 x 10⁵ m², blue), both situated in Wisconsin, USA.</p

Fitted splines for the Generalised Additive Model.

<p>The y-axis is the additive contribution of the spline to the fitted model over the range of the covariate. The smooth functions are subject to centring constraints and are plotted here on different scales for clarity. The shaded region is an approximate 95% confidence interval on the function; however, it excludes uncertainty in the model's constant term, β₀, hence the narrowness of the interval at the “middle” of the distribution for the smooths of altitude and latitude.</p