Summer depth distribution profiles of dissolved CO2 and O2 in shallow temperate lakes reveal trophic state and lake type specific differences

Science of the Total Environment 566–567 (2016) 63–75

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Summer depth distribution profiles of dissolved CO2 and O2 in shallow temperate lakes reveal trophic state and lake type specific differences Alo Laas ⁎, Fabien Cremona, Pille Meinson, Eva-Ingrid Rõõm, Tiina Nõges, Peeter Nõges Centre for Limnology, Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Kreutzwaldi 5, 51014 Tartu, Estonia

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• We measured CO2 and DO profiles in 8 lake types at sub-hourly intervals over a week. • Surface layers of alkaline and dystrophic lake were steadily supersaturated with CO2. • 3 lake types acted as CO2 sinks, another 3 were in equilibrium with atmospheric CO2. • Vertical dissolved gas gradients occurred even in thermally non-stratified lakes. • Differences in trophic state and depth accounted most for gas regime differences.

a r t i c l e

i n f o

Article history: Received 12 February 2016 Received in revised form 5 May 2016 Accepted 6 May 2016 Available online xxxx Editor: D. Barcelo Keywords: Dissolved oxygen Dissolved carbon dioxide Profiles WFD lake types

⁎ Corresponding author. E-mail address: [email protected] (A. Laas).

http://dx.doi.org/10.1016/j.scitotenv.2016.05.038 0048-9697/© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t Knowledge about dissolved oxygen (DO) and carbon dioxide (CO2) distribution in lakes has increased considerably over the last decades. However, studies about high resolution dynamics of dissolved CO2 in different types of lakes over daily or weekly time scales are still very scarce. We measured summertime vertical DO and CO2 profiles at subhourly intervals during one week in eight Estonian lakes representing different lake types according to European Water Framework Directive. The lakes showed considerable differences in thermal stratification and vertical distribution of dissolved oxygen and CO2 as well as different diurnal dynamics over the measurement period. We observed a continuous CO2 supersaturation in the upper mixed layer of the alkalitrophic (calcareous groundwaterfed) lake and the dark soft-water lake showing them as CO2 emitting “chimneys” although with different underlying mechanisms. In three lake types strong undersaturation with CO2 occurred in the surface layer characterising them as CO2 sinks for the measurement period while in another three types the surface layer CO2 was mostly in equilibrium with the atmosphere. Factor analysis showed that DO% in the surface layer and the strength of its relationship with CO2% were positively related to alkalinity and negatively to trophic state and DOC gradients, whereas deeper lakes were characterised by higher surface concentration but smaller spatial and temporal variability of CO2. Multiple regression analysis revealed lake area, maximum depth and the light attenuation coefficient as variables affecting the largest number of gas regime indicators. We conclude that the trophic status of lakes in combination with type specific features such as morphometry, alkalinity and colour (DOC) determines the distribution and dynamics of dissolved CO2 and DO, which therefore may indicate functional differences in carbon cycling among lakes. © 2016 Elsevier B.V. All rights reserved.

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A. Laas et al. / Science of the Total Environment 566–567 (2016) 63–75

1. Introduction Because aquatic primary producers consume CO2 for photosynthesis at roughly the same rate as they release DO into water, and respiration can be seen as a reverse process of that, a strong coupling between DO and dissolved CO2 in lake water could be assumed, at least in productive systems where the intensity of both processes is higher, making their effect on the concentration dynamics of dissolved gases more visible. Field measurements in lakes show that in some cases the dynamics of dissolved gases are indeed caused mainly by metabolism (Johnson et al., 2010.). In most cases, however, the distributions of DO and dissolved CO2 are strongly decoupled for several reasons including CO2 additions from allochthonous organic matter degradation (Jonsson et al., 2003) or volcanism (Jones, 2010), compartmentalisation of lake environments by thermal stratification (Baehr and DeGrandpre, 2004), pH dependence of dissolved CO2 concentrations resulting from the functioning of the carbonate buffer (Marcé et al., 2015; Weyhenmeyer et al., 2015), nitrate or sulphate respiration in anoxic conditions (Liikanen et al., 2002), methanogenesis and methane release by ebullition (Casper et al., 2000), and anoxygenic photosynthesis (Bryant and Frigaard, 2006). Consistent low frequency oscillation in the CO2 partial pressure, DO, and water temperature time-series may also be caused by hydrodynamic processes such as seiches (Baehr and DeGrandpre, 2002). Hence, the question remains, to what extent the distribution and dynamics of dissolved CO2 and DO are controlled by trophic state determining the intensity of lake metabolism and what is the role of lake type specific features such as lake morphometry, alkalinity or water colour in modifying the gas regime. Lakes are frequently super-saturated with CO2 relative to the atmosphere (Cole et al., 1994; Prairie et al., 2002; Jonsson et al., 2003; Kortelainen et al., 2006). CO2 supersaturation can be caused by several alternative processes: by negative net ecosystem production (NEP; Cole et al., 2000), photochemical degradation of dissolved organic carbon (DOC) (Vachon et al., 2016) or high dissolved inorganic carbon (DIC) inflow from surface- or groundwater (Marcé et al., 2015; Weyhenmeyer et al., 2015). Excluding the temperature effect on gas solubility, DO supersaturation in lakes can only be reached by positive net ecosystem production (NEP N 0 if primary production exceeds respiration) occurring during limited time in the growing season when chemical and physical conditions support intensive photosynthesis. Therefore, supersaturation of the surface layer of lakes with dissolved CO2 is more common in various geographic regions than supersaturation with DO. Nowadays advanced sensor technologies are widely applied in aquatic studies. Diurnal variation in water temperature and DO profiles in different lake types is already well-known (Smith and Bella, 1973; Melack, 1982; Gelda and Effler, 2002; Sadro et al., 2011; Obrador et al., 2014). However, direct observations of daily dynamics of dissolved CO2 in lakes are still rare, probably because of the relatively low reliability and accuracy and high cost of CO2 sensors. A robust, accurate and responsive sensor-based method for direct and continuous measurement of dissolved CO2 has been elaborated and tested in tropical, temperate and boreal streams and ponds (Johnson et al., 2010). Regarding lakes, sensor measurements of CO2 have been reported for the upper mixed layers of some lakes (Dinsmore et al., 2009; Johnson et al., 2007; Vachon and del Giorgio, 2014) or for the surface and bottom layers (Baehr and DeGrandpre, 2002, 2004), while we have not found data on continuous profile measurements of CO2. In most of in situ studies (Frankignoulle et al., 2001; Jones and Mulholland, 1998; Hope et al., 2001; Billett and Moore, 2008; Sakagami et al., 2012) dissolved CO2 concentration is still determined by the commonly used headspace method of Kling et al. (1991). As noted by Johnson et al. (2010), attempts to use automated sampling to increase sampling frequency for this method face the problem of degassing of dissolved CO2 in the sample bottle.

In this paper we present the vertical distribution of dissolved CO2 and DO in 8 hemiboreal lake types obtained by direct continuous in situ measurements using optical sensors. Each lake in our selection represents one of the eight lake types in Estonia according to the European Water Framework Directive (WFD) typology (Table 1). Despite Estonia being a small country, the diverse geological setting supports a broad variety of natural lake types. Based on six indicators of the gas regime, we study how the broad range of observed saturation levels of both CO2 and DO and their coupling is related to lake type and trophic state parameters. We demonstrate the existence of gradients in the distribution of dissolved gases even under isothermal conditions of lakes and discuss the potential of vertical CO2 measurements as a method to enhance understanding of carbon dynamics in aquatic environments. 2. Material and methods 2.1. Study sites This study was conducted in eight Estonian lakes (Fig. 1) each belonging to a different lake type according to the EU WFD typology which is based on lake area, alkalinity, conductivity, chloride content, thermal stratification, and colour (Table 1). The two largest lakes in Estonia, Peipsi (3555 km2, fifth largest lake in Europe) and Võrtsjärv (270 km2), form individual types referred to, correspondingly, as VLarge and Large. They were allocated to separate lake types in the WFD compliant lake typology (ME, 2009), because strong wind induced mixing makes them incomparable with smaller lakes in the region, whereas stronger sediment resuspension in the shallower Võrtsjärv causes higher turbidity and light limitation clearly distinguishing it from the deeper Peipsi. The remaining ~ 1200 small lakes are grouped into six types (Ott, 2006; ME, 2009). To make the text easier to follow, the eight lakes in this study are further referred to by their abbreviated type names (Table 1). In addition, the lakes in this study differed also by catchment land use, trophic status, and water retention time (Table 2). All lakes were rather shallow with a mean depth b 10m. Measurements in all lakes were carried out within a 2-month period from July to September 2014. (See Table 3.) Our selection of lakes represented rather adequately the broad range of lake characteristics of the region both by type specific features and along the trophic scale. The size of lakes ranged from 2 ha to N200.000 ha, i.e. over 5 orders of magnitude, Chl a over 3 orders, Kd and TP over 2 orders, and DOC and HCO–3 over one order of magnitude. According to the trophic scale, the lakes ranged from oligotrophic to hypertrophic category. Also the indicator values characterising CO2 and DO distribution had similarly broad ranges. This extensive variability included in all variables was certainly instrumental for the analysis enabling a strong manifestation of their impacts although determined a transformation of most of the variables to reduce the skewness of their distribution. 2.1.1. Alkalitrophic lakes (Alk) Äntu Sinijärv is a source lake fed by karstic ground waters and represents highly alkalitrophic (N290 HCO–3 mg L−1) lakes in Estonia. Lakes of this type are very local in the Pandivere Upland area (only a few elsewhere) and there are only 60 lakes of that type in Estonia (Ott and Kõiv, 1999). With a mean light attenuation (Kd) of 0.16 ± 0.02 m− 1 for the photosynthetically active spectral region measured in 1995–96, Äntu Sinijärv was the most transparent lake in Estonia (Nõges, 2000). 2.1.2. Non-stratified lakes with medium alkalinity (MedAlk) This is the most abundant lake type in Estonia, comprising approximately 1/3 of our lakes (Ott and Kõiv, 1999). These lakes are relatively shallow, with medium water retention times and may exhibit only episodic thermal stratification.

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Table 1 Estonian lake types according to European Water Framework Directive. Type name

Alkalitrophic Non-stratified, medium alkalinity Stratified, medium alkalinity Dark-coloured soft-water lakes Light-coloured soft-water lakes Lake Võrtsjärv Lake Peipsi Coastal lakes

Abbreviation Area km2

Alkalinity HCO–3 mg

Conductivity μS

Cl− mg

L−1

cm−1

L−1

Thermal stratifycation

Colour Pt\ \Co scale

Representative in current study

Alk MedAlk

b10 b10

N240 80–240

N400 165–400

b25 b25

Non-stratified Non-stratified

N/A N/A

Äntu Sinijärv Ülemiste

StratMedAlk

b10

80–240

165–400

b25

Stratified

N/A

Saadjärv

DarkSoft

b10

b80

b165

b25

Non-stratified

≥100°

Valguta Mustjärv

LightSoft

b10

b80

b165

b25

Non-stratified

b100°

Erastvere

Large V-Large Coastal

100–300 80–240 N1000 80–240

165–400 165–400

b25 b25 N25

Non-stratified Non-stratified Non-stratified

b100° b100°

Võrtsjärv Peipsi Mullutu Suurlaht

2.1.3. Stratified lakes with medium alkalinity (StratMedAlk) This is the second most abundant lake type in Estonia, which includes about 1/4 to 1/5 of the total number of lakes. Due to larger depth, these lakes provide more diverse habitats than the non-stratified lakes but the lakes are also more sensitive to human impacts due to potential build-up of anoxic conditions in the benthal leading to release of phosphorus from the sediment and thus a positive feedback to eutrophication. The character and functioning of these lakes are largely determined by the thermally stratifying larger volume of water (Ott, 2010).

The only thermally stratified lake in our selection – Lake Saadjärv – represents deep clear water lakes in Estonia (Zmax = 25 m). 2.1.4. Dark-coloured soft-water lakes (DarkSoft) It has been estimated that about 8% of Estonian lakes are darkcoloured soft-water lakes (Ott and Kõiv, 1999). Lakes under this type are shallow and acidic, contain large amounts of humic matter that contributes to radiative heating in summer (Ott, 2010). Strong light attenuation (up to 11 m− 1) restricts the euphotic layer and limits

Fig. 1. Location of the studied lakes in Estonia.

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Table 2 General description of the study lakes. Values of chemical and optical parameters are the averages of our measurements and earlier data included in the database of the Centre of Limnology. Variable

Alk (Äntu Sinijärv)

Medalk (Ülemiste)

StratMedalk (Saadjärv)

DarkSoft (Valguta Mustjärv)

LightSoft (Erastvere)

Large (Võrtsjärv)

V-Large (Peipsi)

Coastal (Mullutu Suurlaht)

Trophic status Mixing regime Area (ha) Mean depth (m) Max depth (m) TP (μg L−1) TN (μg L−1) Chl a (μg L−1) DOC (mg L−1) HCO–3 (mg L−1) Kd (m−1) Secchi depth (m) Water residence time (y) Watershed size (km2)

Alkalitrophic Polymictic 2.1 6 8 9 345 1 4.72 292.8 0.25 bottom source lake

Eutrophic Polymictic 944 2.5 4.2 48 723 24.7 13.7 201.3 3.5 1.4 0.33

Mesotrophic Dimictic 724.5 8 25 22 414 5.62 9.2 150.47 0.42 4.9 0.13

Hypertrophic Polymictic 20.4 b1 1 242 670 23.19 35.2 30.5 10.34 0.15 source lake

Hypertrophic Dimictic 16.3 3.5 9.7 137 1126 125.64 12.3 99.6 2.96 1.25 0.5

Eutrophic Polymictic 27000 2.8 6 48 910 35.71 11.8 211 2.76 0.55 1

Eutrophic Polymictic 261100 8.3 12.9 47 375 13.4 12 170.8 1.6 2.9 2

Eutrophic Polymictic 412.7 b1 1.7 60 1000 9.04 18.1 109.8 0.58 bottom 0.2

1,37

98.8

28.4

1.34

5.2

3116

47800 (10489 in Estonia)

238

TP — total phosphorous, TN — total nitrogen, Chl a — chlorophyll a, DOC — dissolved organic carbon, HCO–3 — alkalinity, Kd — vertical light-attenuation coefficient.

photosynthesis (Reinart et al., 2000). Among our study this lake type is represented by Valguta Mustjärv, a very shallow 20-ha lake that has partly recovered from a heavy nutrient loading in the 1980s.

2.1.5. Light-coloured soft-water lakes (LightSoft) The study of Ott and Kõiv (1999) shows that light-coloured softwater lakes comprise a little more that 5% of all Estonian lakes. Those lakes are originally oligotrophic or semi-dystrophic, mainly characterised by low productivity, small catchment area implying slow water exchange, low buffering capacity, and weak stress tolerance (Ott, 2010). Because of originally high water transparency, these lakes do not develop stable stratification. If impacted, e.g. by eutrophication, the ecosystems of light-coloured soft-water lakes become strongly destabilized characterised by frequent algal blooms and onset of stratification. In the current study this lake type was represented by the impacted Lake Erastvere which by now has become dimictic and hypertrophic and, unfortunately, cannot characterize the reference state of this lake type.

2.1.6. Coastal lakes (Coastal) Coastal lakes constitute approximately 10% of the total number of Estonian lakes. Their functioning depends strongly on the very irregular marine water inflow, which creates highly variable conditions and unstable biota. Estonian coastal lakes are shallow, with high pH, water transparency and water temperature in summer. The main primary producers are charophytes not phytoplankton. While those lakes are shallow and clear, there is no temperature stratification. In our study this category is represented by Mullutu Suurlaht.

2.2. Manual sampling Manual water samples for laboratory analyses of dissolved organic carbon (DOC), total phosphorus (TP) and nitrogen (TN), carbonate alkalinity (HCO–3), chlorophyll a (Chl a), and phytoplankton were taken from all studied lakes once during the sensor deployment period. For determination of DOC concentrations in lakes the carbon content of the filtrate was measured according to Toming et al. (2013). TP was determined with C. Zeiss spectrophotometer, according to Estonian national standard EVS-EN 1189, TN with Bran + Luebbe autoanalyser, according to EVS-EN ISO 13395. HCO–3 was determined colorimetrically using 0.02% methyl orange test. For Chl a, 0.1–1 l of water was passed through Whatman GF/F glass microfiber filter and concentrations were measured spectrophotometrically in 96% ethanol extracts at a wavelength of 665 nm (Edler, 1979).

2.3. Sensor deployments and monitoring stations All lakes were equipped with a high frequency monitoring platform or small lake buoy (OMC-7012 data-buoy) for a 6 to 12 full day period. Before sensor deployment (described in detail further), water temperature (T, °C), DO and electrical conductivity profiles were measured with handheld multiparametric sonde (YSI ProPlus). The location of the metalimnion was determined from water temperature profile according to Wetzel (2001). Continuous monitoring of DO concentration and water temperature was performed by an automated station equipped with a multiparametric sonde (Yellow Springs Instruments (YSI) 66,002–4) at one-meter depth. Additional sensors for DO/temperature (Ponsel

Table 3 Data collection periods and weather conditions during measurements. Lake

Alk MedAlk StratMedAlk DarkSoft LightSoft Large V-Large Coastal a

Measurement period

15.07–22.07.2014 16.07–23.07.2014 18.08–27.08.2014 23.07–05.08.2014 19.08–29.08.2014 23.07–05.08.2014 09.09–16.09.2014 07.08–14.08.2014

Full days of measurement

7 6 9 12 10 12 7 7

Data from closest meteorological station to the lake.

Full days with parallel DO and CO2 data

6 1 5 4 9 4 6 6

Range (and average) of daily mean meteorological variables during measurementsa Wind speed, m s−1

Air temperature, o C

PAR, μmol m−2 s−1

1.2–3.4 (2.4) 1.6–2.1 (1.9) 1.7–4.1 (2.6) 2.5–3.4 (2.9) 1.4–4.4 (2.8) 2.5–3.4 (2.9) 0.6–2.8 (1.3) 1.9–7.0 (4.5)

17.4–19.8 (18.7) 20.1–22.2 (21.2) 13.0–14.3 (13.5) 22.6–24.7 (23.7) 12.4–15.7 (13.9) 22.6–24.7 (23.7) 9.9–16.6 (13.8) 17.1–21.9 (20.4)

324–619 (517) 578–608 (593) 55–397 (270) 337–452 (419) 55–391 (263) 337–452 (419) 179–350 (251) 152–525 (358)

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Table 4 Observed extreme values of gas regime indicators in lakes over the selected 24h period. Cases where CO2 and DO showed similar patterns are highlighted.

Characteristic

Saturation level

Indicator

Minimum

Lake

CO2% surface

1

1835

Alk

80

DarkSoft

144

Alk

CO2% bottom

114

V-Large

7047

0

LightSoft

114

V-Large

6991

LightSoft

DO% vertical difference

0

Coastal

102

LightSoft

CO2% vertical gradient

8

V-Large

4372

DO% vertical gradient

0.2

V-Large

33.5

DarkSoft

74

DarkSoft

CO2% stdevsurface

Relative temporal variability (CV%)

Correlation (Pearson r)

LightSoft

67

CO2% vertical difference

Absolute temporal variability (St.dev)

Coastal

Lake

DO% surface

DO% bottom

Spatial variability

Maximum

Coastal

Coastal

4

Coastal

DO% stdev surface

2

StratMedAlk

CO2% stdevbottom

18

V-Large

768

MedAlk

DO% stdevbottom

0

LightSoft

38

MedAlk

276

Coastal

8

Coastal

9

Coastal

CO2% cv surface

2

Alk

DO% cv surface

2

StratMedAlk

CO2% cv bottom

3

StratMedAlk

DO% cv bottom

0

LightSoft

115

Medalk

Coastal

-0.7

DarkSoft

DO%–CO2%correlation surface

-0.1

OPTOD) and dissolved CO2 concentration (AMT Analysenmesstechnik GmbH) were used at several depths. In all lakes the upper sensors were placed at 0.5 m depth and the position of other sensors was

67

Large

decided depending on stratification to adequately characterize all different layers (see Figs. 2–4 for details). In the deepest lake (StratMedalk) a chain of 12 HOBO Pendant temperature loggers was

Table 5 Multiple regression results over the selected 24h period. X — significant exploratory variables (highlighting is used to better visualize the pattern). n.s. — non-significant variables selected by forward stepwise procedure. Abbreviations: DO% — dissolved oxygen saturation, CO2% — dissolved carbon dioxide saturation, DOC — dissolved organic carbon, TP — total phosphorus, Chl a — chlorophyll a, Kd — vertical light-attenuation coefficient, cv — coefficient of variation, corr — Pearson correlation coefficient, vert. diff. — vertical, diff. — difference, grad — gradient (change m−1).

Dependent variable Characteristic

Location

LogCO2% Surface

DO%

Saturation LogCO2%

level Bottom

DO%

LogArea

Bottom

column

DO% vert. diff.

variability

n.s.

X

X

column Surface

p

0.626

0.145

0.752

0.132

0.915

0.017

0.673

0.061

0.809

0.056

0.746

0.004

n.s.

0.806

0.022

n.s.

0.203

0.324

X

0.908

0.019

X

0.681

0.058

0.199

0.149

DO% vert. DO% & CO2%

LogKd n.s.

X X

n.s.

n.s.

X

X n.s. n.s. n.s.

X X

X

n.s. X

X

n.s.

grad. m–1 grad. m–1

(Pearson)

n.s.

X

n.s.

CO2% vert. Water

HCO3–

X

DO%

diff.

LogDOC

n.s.

X

DO %

CO2% vert. Water

Correlation

LogChl α

CO2%

variability

(CV%)

LogTP

n.s.

Spatial

Temporal

LogMax d

n.s.

CO2% Surface

r2

Explanatory variables Indicator

X

n.s.

X

0.765

0.032

X

X

0.894

0.002

68 A. Laas et al. / Science of the Total Environment 566–567 (2016) 63–75 Fig. 2. Lake water temperature distribution over the study period. Note that the colour scales differ by lakes. Black dots represent sensor position in the water-column. Horizontal bold line above date represents the typical 24-h period that was selected for statistical analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)

A. Laas et al. / Science of the Total Environment 566–567 (2016) 63–75 Fig. 3. Distribution of DO saturation in lakes. Note that the colour scales differ by lakes. Black dots represent sensor position in the water-column. Horizontal bold line above date represents the typical 24-h period that was selected for statistical analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)

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70 A. Laas et al. / Science of the Total Environment 566–567 (2016) 63–75 Fig. 4. Distribution of dissolved CO2 saturation in lakes. Note that the colour scales differ by lakes. Black dots represent sensor position in the water-column. Horizontal bold line above date represents the typical 24-h period that was selected for statistical analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the online version of this chapter.)

A. Laas et al. / Science of the Total Environment 566–567 (2016) 63–75

used reaching from 0.5 to 18 m depth. The YSI multiparametric sondes were equipped with a self-cleaning optical sensor for DO and turbidity, all other sensors were cleaned manually after one-week period. All sensors were calibrated at the beginning and at the end of deployments. No drifts in the sensors were observed between calibrations. Sensors performed automatic measurements every 10 to 30 min (depending on power availability at each lake) at up to four different depths during the study period. Data collection on buoys and platforms was controlled with OMC-045-II GPRS data loggers. Automated stations were mostly placed near the deepest area in each lake. In Võrtsjärv (Large) our buoy was located close to our long-term monitoring point which, according to Nõges and Tuvikene (2012), is representative for N 90% of the lake area. In Peipsi (V-Large), because of security reasons, measurements were made at a station in the Mustvee bay approximately 1 km from the western shore. 2.4. Measurement and calculation of dissolved CO2 values Membrane covered optical CO2 sensors (AMT Analysenmesstechnik GmbH) with measuring ranges of 30 mg L−1 and 80 mg L−1 were used to record dissolved CO2 partial pressure (pCO2) values in lakes. According to the sensors' manual (http://www.amt-gmbh.com/), the inner sensor volume is separated from the sample by means of a gas permeable silicone membrane, non-passable for liquids and solids. If the sensor is immersed into a sample, a pCO2 equilibration is achieved between the inner sensor volume and the sample. A Single-Beam Dual Wavelength nondispersive infrared (NDIR) optical sensor mounted inside the sensor detects the dissolved CO2 gas, but is insensitive to carbonate and bicarbonate. Measurement of pCO2 is accompanied by water temperature and air pressure measurements to calculate CO2 concentrations in the lakes. Increases in water temperature cause a decrease in sensor output, while increases in atmospheric pressure cause an increase in sensor output. All sensors had a fixed measuring depth, therefore we could do the depth correction for each measurement time interval once for all. We assumed a constant atmospheric pCO2 of 400 μatm (http://co2now.org/) which was taken as the equilibrium value for the air-water interface. Although DO and CO2 measurements in all lakes lasted between 6 and 12 days, in some lakes (DarkSoft and Large) we could not capture parallel data on both gases for N4 days because of malfunctioning of devices. In MedAlk parallel measurements of CO2 and DO at all depths succeeded for one full day only (Table 3). In StratMedAlk with a maximum depth of 25 m we could measure temperature down to 18 m and DO and CO2 only down to 10 m depth due to limited length of the cables. 2.5. Statistical analysis In order to analyse relationships between lake type/trophic state characteristics and the measured gas distribution patterns, we first selected visually the most typical 24-h period from each lake (marked in Figs. 2–4 with a bold horizontal line above date). This was done to reduce noise caused by meteorological disturbances and possible technical issues concerning single lakes and single days. For this 24h period we calculated the following indicators for both DO and CO2 to characterize the gas regime of lakes: average saturation level (DO%, CO2%) and coefficient of variation (DO% cv, CO2% cv) at the surface (0.5 m), and at the bottom (data from the deepest sensor), vertical difference (DO% vert diff, CO2% vert diff) and vertical gradient (DO% vert grad, CO2% vert grad, m−1) of the average saturation level, and the Pearson correlation coefficient between DO% and CO2% at the surface. Among lake type specific characteristics, we selected lake area, mean, maximum and relative depth (calculated according to Wetzel (2001)), HCO–3 and DOC. The trophic state of lakes was characterised by TP, TN, Chl a and Kd. Variables with a log-normal distribution were included in the analysis as logarithmic values. A correlation analysis was carried out to select the best

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candidate variables for the multiple regression analysis (see Supplementary materials, Table S1). We used the forward stepwise procedure for selecting the variables for the model. As several explanatory variables such as TP, Chl a, Kd and the different expressions for depth were strongly correlated, we carried out a Principal Component Analysis (PCA). All statistical analyses were performed using the STATISTICA analysis software (Dell Inc., 2015). Contour graphs were produced using SigmaPlot 12.0 (Systat Software, Inc. GmbH). 3. Results 3.1. Stratification in studied lakes No stable thermal stratification was observed in most lakes in summer because of their shallowness. Although 7 out of 8 Estonian lakes types according to the WFD typology should not stratify (Table 1), 3 of the type representative lakes were strongly stratified and 3 others stratified occasionally and weakly during our study period (Fig. 2 and Fig. S1 in Supplementary materials). Only the V-Large and the Coastal were always mixed. Due to exceptionally calm weather during measurements in Large (Table 2), considerable vertical temperature differences occurred in this lake ranging from 1.3 to 5.75 °C. Although some lakes did not exhibit a stable thermal stratification or were fully mixed during our study, they were all stratified for dissolved gases (Fig. 3; 4; Figs. S2; S3) with the strongest stratification occurring in two deeper lakes (StratMedAlk and LightSoft), but also in the shallow Alk. In the stratified conditions in LightSoft there was a strong bloom caused by the cyanobacterium Aphanizomenon gracile Lemm at the beginning of our study. 3.2. Gas distribution in studied lakes Comparing the gas distribution indicators for the selected most typical 24h period (Table 4; Fig. S4), the highest concentration of dissolved CO2 was measured at the bottom of LightSoft lake (7047% relative to equilibrium concentration with atmosphere), followed by Coastal lake (4373%, not shown). The lowest bottom concentrations of dissolved CO2 (114%) but also the smallest vertical difference and gradient were measured in V-Large. We observed a continuous CO2 supersaturation in the upper mixed layer of Alk reaching 1835% and DarkSoft (607%); all other lakes had at least short periods when the surface of the lake was undersaturated. All eight lakes had at least a short-term stratification for dissolved CO2 (Fig. 3) while in the 4 shallower lakes the concentrations were temporarily fully uniform. The largest difference between CO2 saturation levels at the surface and near bottom occurred in the LightSoft but the gradient was larger in the shallow Coastal lake (Table 4; Fig. S4). The largest diurnal variability in CO2 saturation (%) in the surface layer (standard deviation, SD) occurred in DarkSoft (74%) and Alk (46%) where the surface waters were constantly supersaturated with CO2. The relatively strongest diurnal variability was measured in the Coastal lake (CV = 276%) followed by that in LightSoft (45%) and VLarge (30%). We found several similarities between the occurrences of extremes of CO2 and DO in lakes. Both gases had their highest surface layer saturation levels in Alk, the largest vertical differences occurred in the stratified LightSoft and the smallest vertical gradients in V-Large. Both gases showed the largest bottom layer SD in MedAlk and the largest surface layer variation coefficients in Coastal. Highest DO saturation levels were measured in the surface layer of the Alk (144%) but reached nearly 120% also in the eutrophic Coastal, Large, and MedAlk. Unlike other lakes where the highest values of DO were measured near the surface, in V-Large the maximum occurred at 1.5 m depth. The highest vertical difference of DO was measured in LightSoft, which was strongly stratified for both gases. DO saturation in this lake ranged from 102% at the surface to anoxia at 2.8 m depth. The lowest

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Fig. 5. Strength of the relationship (R2) between DO and CO2 concentrations in the surface layer (0.5 m) and the trophic state parameters total phosphorus (TP), chlorophyll a (Chl) and light attenuation coefficient (Kd) in the studied lakes.

vertical DO gradient was captured in V-Large where the saturation level fluctuated slightly around 100%. The highest DO temporal variation both in absolute and relative terms occurred in Coastal for surface layers and MedAlk for bottom layers. The strength of the negative correlation between DO and CO2 in the surface layer scaled well with the trophic state of lakes being the strongest in hypertrophic DarkSoft (r = −0.73) and decreasing towards the oligotrophic Alk (r = − 0.22). The only exception was the eutrophic Coastal lake where the correlation was weak too (Fig. 5). According to the multiple regression analysis (Table 5), Kd and lake area appeared among significant determinants for the largest number of gas distribution indicators, and were included in the models of, correspondingly, 6 and 5 out of 13 indicators tested. Also the maximum depth of lakes explained a large proportion of variability included in the gas distribution data. Both HCO–3 and Chl a were significant determinants in two models, DOC in one, while TP was not selected as a

significant variable for any of the gas distribution indicators. The best models, including the ones for CO2 saturation in the bottom layer, CO2 vertical difference and the strength of the DO–CO2 relationship, explained approximately 90% of the variance in these dependent variables using morphometric and trophic state variables. The two first factors of the PCA (Fig. 6) explained, respectively, 42% and 21% of the total variance included in the data. Kd, TN, TP, Chl a and DOC all had highest positive loadings to F1 that justified calling it a “trophic state factor”, whereas alkalinity had the highest negative loading to F1. Among gas distribution indicators, vertical gradients of both CO2 and DO were positively related to the trophic state factor. Among lakes, the two hypertrophic softwater lakes (DarkSoft and LightSoft) were positively related to the trophic state factor, while the Alk, which can be considered oligotrophic by most trophic state indicators, remained on the negative side of F1. Total nitrogen and lake area had the highest (negative) loadings to the second PCA factor F2. The “size factor” was positively associated with DO saturation both at the surface and bottom and negatively with CO2 saturation in the surface layer. Among lakes, Large, V-Large and Coastal were positively associated with F2 and all small lakes remained on its negative side. The only exception was StratMedAlk, which grouped together with smaller lakes. 4. Discussion 4.1. Type and state characteristics The European Water Framework Directive (EU, 2000) clearly distinguishes between natural lake type descriptors that characterize type specific biological reference conditions, and status descriptors describing the anthropogenic impact which tends to deviate the ecosystem from its pristine state. Lake type descriptors are divided into obligatory descriptors, such as geographic coordinates, depth, size and basin geology (calcareous, siliceous, organic) and optional descriptors which may include for instance lake shape, residence time, mixing characteristics, alkalinity etc. As the mixing type is defined for lakes in reference conditions, it may happen that with increasing Kd due to eutrophication, siltation or brownification, the mixing type changes (Heiskanen et al., 2015). A predominant change would be that polymictic lakes start developing a more stable thermal stratification. Among our study lakes, this was certainly the case for LightSoft which being oligotrophic and polymictic by origin (reference conditions) has turned into a stratified hypertrophic lake. We observed stronger stratification than expected by type description also in Alk and MedAlk. In order to avoid mixing up lake type specific effects with trophic state effects and concentrate only on the former, an alternative would have been to study only reference lakes, i.e. the pristine lakes without human impacts. That, however, would have deviated us from the real life where most of the water bodies are more or less impacted by cultural eutrophication (Smith and Schindler, 2009). 4.2. General patterns

Fig. 6. Results of the Principal Component Analysis relating indicators of DO and CO2 distribution with lake type and trophic state parameters. Both groups of variables are projected on the 1 × 2 factor plane as empty dots, lakes as filled dots. Abbreviations: DO — dissolved oxygen, DOC — dissolved organic carbon, TN — total nitrogen, TP — total phosphorus, Chl a — chlorophyll a, Kd — diffuse light attenuation coefficient, grad — gradient (change m−1), surf — surface.

The “trophic state” factor remained clearly the strongest determinant for the gas distribution in our set of lakes. However, certain typespecific peculiarities can be drawn out for individual lakes. The multiple regression analysis revealed lake area as one of the key variables determining the dynamics of several gas distribution indicators. Thus its only moderate loading to F2 in the PCA was rather unexpected. In summer, the surface layers of most Estonian lakes were supersaturated for both DO and CO2 similarly to several lakes elsewhere (Dinsmore et al., 2009; Vachon and del Giorgio, 2014) and therefore contributed to CO2 emissions to the atmosphere. In line with patterns described by Dinsmore et al. (2009) and Ducharme-Riel et al. (2015), the supersaturation with CO2 in Estonian lakes typically increased gradually with depth, reaching its maximum near the lake bottom. High CO2

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concentration near the bottom suggests CO2 diffusion from the sediment (Rantakari and Kortelainen, 2005). The surface layer was undersaturated with CO2 in three of our studied lakes (V-Large, LightSoft and Coastal) during most of the study period likely caused by photosynthetic CO2 uptake that enables a downward flux of CO2 from the atmosphere to the lake. As the Estonian part of V-Large with an area of 1442 km2 makes up about 2/3 of the total area of Estonian lakes (2130 km2), we can claim that during our study, in a major part of Estonian lake surface area CO2 uptake was predominating over release. Our earlier studies (Rõõm et al., 2014) have shown a mid- and late summer CO2 uptake also in the pelagic part of Large in case of similar weather conditions as in 2014. 4.3. Individual features of lakes A clear drawback of this selection of lakes was that the small number of lakes predetermined a unique combination of type and trophic state characteristics. So for example, the clustering of DOC (type characteristic) together with TP and Chl a (trophic state characteristics) in the factor analysis was clearly caused by the specific features of the DarkSoft lake type. Although a large number of dystrophic (humic) lakes have been affected by eutrophication over the last half century (Ott and Kõiv, 1999), the combination of dystrophic and eutrophic features can still be due to the specific characteristics of the lakes in this study. The highest trophic state DarkSoft combined with the highest DOC content among the study lakes determinedmainly its gas regime and the strong synchrony of the opposite changes in DO and CO2 dynamics. Fast radiative heating and cooling of the dark water caused the surface temperature to fluctuate by 4 °C daily that likely contributed to the large amplitude of DO and CO2 saturation through changes in gas solubility. Decomposition of allochthonous organic matter was the likely reason causing low DO and continuous high supersaturation with CO2 even in the surface layer. This kind of lakes form a “chimney” type in which CO2 supersaturation is based on high organic C respiration rates (Cole et al., 1994; Jonsson et al., 2003). A phenomenon that also can be attributed to Alk is the positioning of HCO–3 to the negative end of the “trophic state” factor in the PCA. High alkalinity itself is not contradicting with high trophic state as two eutrophic lakes included in our selection, MedAlk and Large, had HCO–3 values exceeding 200 mg L−1. However, the main feature that makes the Alk distinct is the predominant ground water feeding of this lake type. During percolation of water through the karstic area, virtually all phosphorus is bound to calcium maintaining the oligotrophic character of this lake type (Ott and Kõiv, 1999) despite the vicinity of areas of intense agriculture. Even high TN values sometimes measured in this lake (Ott and Kõiv, 1999) have not changed its nature. Although the Alk lake type is defined as non-stratified, also earlier studies have revealed considerable vertical temperature differences ranging from 2.7 to 10.5 °C with a maximum gradient of 7.3 °C m−1 located mostly within the upper 1–2 m layer (Nõges and Nõges, 1998). High surface DO% values in this lake were unexpected as the spring water feeding the karstic lake is not saturated with oxygen and lake has very low phytoplankton biomass, Chl a and planktonic primary production. Previous measurements (Nõges and Nõges, 1998) showed that the extremely low primary production (b2 mg C m−3h−1) was usually restricted to 1–1.5 m layer, although the irradiance level in the transparent water could enable photosynthesis in the whole water column. Because the bottom of the representative of the Alk type is covered with Chara, a large part of primary production and water oxygenation can be attributed to benthic communities (Cremona et al., in press). Besides photosynthesis, the DO supersaturation in the surface layer can be partially caused by the warming of the surface layer in which the equilibration with the atmospheric partial pressure is not immediate in sheltered conditions created by the forest surrounding the small lake. It is probable that dissolved inorganic carbon which enters the lake through a number of carbonate-rich groundwater springs was

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responsible for the highest and most stable CO2 supersaturation levels (up to 1800%) in the surface layer of Alk compared to our other studied lakes. Reverse weathering of calcium carbonate going on in the lake is evidenced by strong calcite precipitation (a visual observation of the lake bottom revealed that submerged plants were covered by a thick calcite crust). Calcite precipitation releases CO2 in amounts exceeding by large the uptake abilities of the vegetation, turning the lake into another CO2 emitting “chimney” types (Marcé et al., 2015; Weyhenmeyer et al., 2015). Gas distribution in MedAlk was likely mostly determined by its eutrophic state. Our study showed that even in virtually homothermal conditions, both DO and CO2 concentration gradients could build up near the bottom of the lakes overnight, probably because of high respiration rates near the sediment. Another reason for DO gradients in these lakes is the uneven vertical distribution of photosynthesis: most of the DO that is produced near the surface may not reach the bottom layers during windless days. A deeper and stratified water column determined the main characteristics of StratMedAlk. Despite the size of the lake (7 km2), the PCA showed that its gas regime was more similar to smaller lakes that could be attributed to its higher water column stability. During most of the time, DO showed a clinograde profile being uniformly distributed within the epilimnion and declining rapidly to zero within the upper 1 m of the metalimnion. We could capture most of the changes, except perhaps the possible increase in CO2 concentration in the unmeasured hypolimnion. We observed also a short deep mixing episode in this lake (on 25th of august) after which the stratification was fully restored (Figs. 3 & 4). The cyanobacteria bloom in LightSoft was the likely reason that in good light conditions caused the DO saturation in the epilimnion to increase up to 120% and fully depleted this layer of CO2. Because of strong stratification, the hypolimnetic CO2 was unavailable for photosynthesis which had to rely mostly on CO2 from respiratory release and diffusion from air. Stronger winds on 25–26 August 2014 (up to 10 m s−1) deepened the thermocline bringing up less oxygenized water but not considerably alleviating the lack of CO2 in the epilimnion. Given the large wind exposed surface area of the Large and V-Large lakes, we expected vertically isothermal conditions in these lakes and fast equilibration of gas partial pressures with the atmosphere. However, the considerable vertical temperature differences occurring in Large due to calm weather created atypically large inhomogeneity also in DO and especially CO2 saturation levels. In V-Large where the measurements were carried out in September, the temperature and gas distributions were more homogeneous, typically to large lakes. The contrasting CO2 ranges between the lakes could partially be explained by different sediment composition at measuring stations: sand in V-Large and organic-rich sediments in Large. Both lakes showed marked diurnal patterns in temperature and gas regimes more manifested, however, in the V-large than in Large. Both large shallow lakes consumed most of the dissolved CO2 available for photosynthesis, creating an equilibrium state (100% saturation) at the air-water interface in Large but undersaturated conditions (10–70% saturation) in V-Large. The difference in CO2 saturation levels showed that during the measurement period, V-Large was likely taking up CO2 from the atmosphere, but in Large the respiratory CO2 release provided enough carbon to support photosynthesis, i.e. the two processes were more or less balanced. Our earlier studies (Rõõm et al., 2014) have shown a mid- and late summer CO2 uptake also in the pelagic part of Large, like we found in this study for lake V-Large. Laas et al. (2012) and Cremona et al. (2014) showed that Large turns from autotrophic to heterotrophic type of metabolism around mid-summer, but autotrophic periods in early August are still commonplace in this lake. The coupling of the “shallowness factor” with the large vertical gradient of CO2 can obviously be attributed to the Coastal where, despite of small depth, a strong vertical gradient in CO2 was likely caused by respiration of the rich Chara mat in this eutrophic lake. Weyhenmeyer et al.

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(2015) conclude that in boreal lakes CO2 is concentrated in bottom waters throughout the year, although these lakes are typically shallow with short water retention times. Our measurements showed neither temperature nor DO stratification in the Coastal lake, although the diurnal variation of both gases was among the highest. The strong vertical gradient in CO2 was likely caused by the dense Chara mat on the bottom of this lake (Cremona et al., in press). The strong CO2 accumulation in the bottom layer caused a decoupling of the DO CO2 relationship which was strong in other eutrophic lakes. 5. Conclusions The eight lakes representing different lake types in Estonia, most of them widespread in the whole northern temperate region, showed considerable differences in thermal stratification and vertical distribution of dissolved oxygen and CO2 as well as different diurnal dynamics over the 1–2 weeks of high-frequency measurements. These differences could mostly be attributed to different trophic state of the lakes and to a lesser extent to lake type specific characteristics such as morphometry and water chemistry. With increasing trophic state, the negative coupling between DO and CO2 grew stronger suggesting trophic state was a good proxy of gas uptake and release processes. Strong dependence of several gas distribution indices on lake area and depth refers to morphometry of the lakes as a complex of factors affecting the redistribution of gases within the water column and the exchange rate with the atmosphere. Stronger stratification of impacted lakes compared to those in reference conditions has strong implications on the gas regimes exacerbating anoxia, internal phosphorus release and accumulating large amounts of CO2 in the bottom layers of lakes, especially in the light of an ongoing global climate warming. Among other type specific factors alkalitrophic groundwater feeding and high DOC loads in the dystrophic lake were the likely reasons causing continuous CO2 supersaturation in the upper mixed layer of these lake types showing them as CO2 emitting “chimneys” but with totally different underlying mechanisms. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2016.05.038. Acknowledgements This research was inspired by GLEON (Global Lake Ecological Observatory Network) and NETLAKE (Networking Lake Observatories in Europe, COST Action) and was funded by the Estonian Ministry of Education and Research (IUT 21-02; PUT 777), Estonian Science Foundation (grant 9102, ETF8486), MARS project (Managing Aquatic ecosystems and water Resources under multiple Stress) funded under the 7th EU Framework Program, Theme 6 (Environment including Climate Change), Contract No.: 603378 (http://www.mars-project.eu) and Swiss Grant for Program “Enhancing public environmental monitoring capacities”. References [EU] European Union, 2000. Directive 2000/60/EC of the European Parliament and of the council of 23 October 2000 establishing a framework for community action in the field of water policy. Off. J. L 327, 1.71. [ME] Ministry of the Environment, 2009. Procedure for the establishment of bodies of surface water and a list of the bodies of surface water the state of which is to be established, classes of the states and the values of quality indicators corresponding to these state classes, and the procedure for the establishment of the classes of state (RTL 2009, 64, 941). Order from 28.07.2009 no. 44. Ministry of the Environment (www.riigiteataja.ee/akt/13210253) [in Estonian], 11 pp. Baehr, M.M., DeGrandpre, M.D., 2002. Under-ice CO2 and O2 variability in a freshwater lake. Biogeochemistry 61, 95–113. Baehr, M.M., DeGrandpre, M.D., 2004. In situ pCO2 and O2 measurements in a lake during turnover and stratification: observations and modeling. Limnol. Oceanogr. 49 (2), 330–340.

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