Science of the Total Environment 520 (2015) 13–22

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

Temporal evolution of organic carbon concentrations in Swiss lakes: Trends of allochthonous and autochthonous organic carbon J.C. Rodríguez-Murillo a,⁎, M. Filella b a b

Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo., E-28006 Madrid, Spain Institute F.-A, Forel, University of Geneva, Route de Suisse 10, CH-1290 Versoix, Switzerland

H I G H L I G H T S • • • • •

Compilation and analysis of time series of organic carbon (OC) in 34 Swiss lakes New approach developed for obtaining allochthonous and autochthonous OC time series. Total OC concentrations generally increase in big lakes and decrease in smaller ones. Autochthonous OC decreases or increases depending on the lake. Allochthonous OC increases in eight of nine big lake stations studied.

a r t i c l e

i n f o

Article history: Received 20 October 2014 Received in revised form 24 February 2015 Accepted 25 February 2015 Available online 14 March 2015 Editor: F. Riget Keywords: Lakes Switzerland Carbon cycle Organic carbon DOC Temporal trends OC origin

a b s t r a c t Evaluation of time series of organic carbon (OC) concentrations in lakes is useful for monitoring some of the effects of global change on lakes and their catchments. Isolating the evolution of autochthonous and allochthonous lake OC might be a useful way to differentiate between drivers of soil and photosynthetic OC related changes. However, there are no temporal series for autochthonous and allochthonous lake OC. In this study, a new approach has been developed to construct time series of these two categories of OC from existing dissolved organic carbon (DOC) data. First, temporal series (longer than ten years) of OC have been compiled for seven big Swiss lakes and another 27 smaller ones and evaluated by using appropriate non-parametric statistical methods. Subsequently, the new approach has been applied to construct time series of autochthonous and allochthonous lake OC in the seven big lakes. Doing this was possible because long term series of DOC concentrations at different depths are available for these lakes. Organic carbon concentrations generally increase in big lakes and decrease in smaller ones, although only in some cases are these trends statistically significant. The magnitude of the observed changes is generally small in big lakes (b 1% annual change) and larger in smaller lakes. Autochthonous DOC concentrations in big lakes increase or decrease depending on the lake and the station but allochthonous DOC concentrations generally increase. This pattern is consistent with an increase in the OC input from the lakes' catchments and/or an increase in the refractoriness of the OC in question, and with a temporal evolution of autochthonous DOC depending on the degree of recovery from past eutrophication of each particular lake. In small lakes, OC dynamics are mainly driven by decreasing biological productivity, which in many, but not all cases, outweighs the probable increase of allochthonous OC. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Over the past decades, increases in dissolved organic carbon (DOC) concentrations have often been reported in rivers and lakes in the Northern Hemisphere (Evans et al., 2005; Clark et al., 2010; Filella and Rodríguez-Murillo, 2014 and references therein). Long-term DOC increases may have a significant impact, not only on the global carbon ⁎ Corresponding author. E-mail addresses: [email protected] (J.C. Rodríguez-Murillo), montserrat.fi[email protected] (M. Filella).

http://dx.doi.org/10.1016/j.scitotenv.2015.02.085 0048-9697/© 2015 Elsevier B.V. All rights reserved.

cycle, but also on freshwater food chains and the quality of drinking water. Lakes are particularly suitable ecosystems for detecting of global change because they are sensitive to climate and integrate the effects of climate and other environmental changes occurring both in the lakes themselves and in their catchments (Adrian et al., 2009; Schindler, 2009; Shimoda et al., 2011). Temporal trends have been mainly studied in small lakes (surface areas of the order of one km2 or smaller), located in a limited number of geographical areas and belonging to peaty, forested catchments and/or areas recovering from acidification (Filella and Rodríguez-Murillo, 2014). Scant analysis has been done of long temporal series of DOC data in bigger lakes and in lakes located in

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J.C. Rodríguez-Murillo, M. Filella / Science of the Total Environment 520 (2015) 13–22

other latitudes, to which the above mentioned constraints do not apply. Moreover, different strategies have been adopted for sampling (i.e., sampling at one fixed depth, integrated sampling) and data treatment (i.e., single point data, data averaging), which does not necessarily lead to comparable results. To our knowledge, trends at different depths have never been reported. For a number of reasons, Swiss lakes offer an ideal place to extend existing knowledge: (i) a number of medium-size and big lakes exist; (ii) Swiss catchments, particularly those of big rivers and lakes, show a variety of land uses with no forest or peat dominance; (iii) acidic critical loads in Switzerland are still exceeded, in spite of decreases of acidic deposition (Augustin and Achermann, 2012), but effects of acidification have not been significant in the country (Pannatier et al., 2011); (iv) long-time data series of hydrological parameters (e.g., temperature, pH, conductivity, dissolved O2, orthophosphate, organic carbon (OC)) exist and are available. In particular, as discussed below, the existence of OC data at different depths in big Swiss lakes can help to get a further insight into drivers of global change. Lakes receive OC from soils in their catchments through underwater flows, rivers and direct surface runoff (allochthonous OC) but also transform inorganic carbon into OC via primary production (autochthonous OC). Both sources can be impacted by global change, and not necessarily in the same direction. Temporal trends of allochthonous OC depend on soil organic carbon (SOC) properties (Jennings et al., 2010). Soil organic carbon is the biggest OC reservoir in the biosphere and a fraction of it is labile or could become so if mobilized (Marín-Spiotta et al., 2014). Besides direct mineralization, an important mobilization process of SOC is through groundwater. The OC is carried away by the rivers and, before eventually ending up in the sea, it undergoes many physical, chemical and biological processes (Aufdenkampe et al., 2011). SOC fluxes can be disturbed by a variety of global change-related drivers such as changes in air temperature, atmospheric CO2, and rainfall patterns (Evans et al., 2005). Autochthonous OC can be affected by global change via increases in temperature (e.g., changes in primary production and mineralisation) and changes in the trophic state of lakes (Tadonléké, 2010), as well as through eutrophication-related processes. Since allochthonous and autochthonous OC sources could be affected differently by global change, it would be interesting to be able to distinguish between them. In principle, this is possible by following C/N ratios, isotopic composition (Bade et al., 2007), DOC fractionation (Imai et al., 2001) or spectroscopic properties (McKnight and Aiken, 1998; McKnight et al., 2001; Anderson and Stedmon, 2007; Bade et al., 2007), but no temporal series of these parameters exists. Temporal trends of autochthonous OC can be approximated by temporal trends in chlorophyll a but the relationship of chlorophyll a with primary production and hence with DOC (Tadonléké, 2010) can be affected by changes in lake phytoplankton communities and for many lakes no complete chlorophyll a data series are available. In this study, we follow the temporal trends of OC in big and small Swiss lakes and take advantage of the existence of long data series at different depths in big lakes to estimate the temporal dynamics of autochthonous and allochthonous DOC. Thingstad et al. (1997) have already pointed out that the difference between DOC concentrations in surface and deep marine waters could be taken as an estimate of potentially degradable DOC but this approach has never been attempted before in lakes. 2. Materials and methods 2.1. Study sites Organic carbon concentration data were furnished by the Swiss Federal Office for the Environment (FOEN) except for DOC in Geneva Petit lac furnished by the Service de l'écologie de l'eau (SECOE) of Canton Geneva (Switzerland) and POC for Geneva Grand Lac given by INRA (Thonon, France). We have selected temporal series for 34 Swiss lakes,

including seven of the 13 Swiss lakes with a surface area larger than 20 km2 (hereafter referred to as ‘big lakes’), and 27 smaller lakes, two of them artificial reservoirs (hereafter referred to as ‘small lakes’). The seven big lakes studied (Geneva, Constance, Neuchâtel, Lucerne, Zurich, Bienne and Morat) are peri-alpine, of glacial origin. Most of the 27 small lakes are located on the Swiss plateau; only three are in the Vaud Alps (Bretaye, Chavonnes et Lioson) while three more are in the Jura Mountains (Brennet, Joux and Taillères). No suitable series for lakes in the southern part of Switzerland were furnished by FOEN but a study on DOC trends in Lake Maggiore has been published (Bertoni et al., 2010). Only series containing 10 or more years of data have been selected. Only the records with the entire set of OC concentration at every lake depth have been considered. Obvious errors have been corrected. Since OC concentrations are strongly seasonal, when possible, in each lake, data from the same months have been taken each year to construct a suitable time series. Where this was not possible, data have a maximum of ±1 month difference. Other lake data (pH, conductivity, water temperature, orthophosphate, and oxygen concentrations as well as, in some cases, chlorophyll a concentration) are also available. There are nine time series in seven big lakes and 30 time series in 27 small lakes. Their location is shown in Fig. 1. Lake Geneva has two sampling stations; they will be referred to as ‘Geneva-big’ (309 m deep, Grand lac) and ‘Geneva-small’ (70 m deep, Petit lac) hereafter. The two sampling locations in Lake Constance are Constance–Obersee and Constance–Untersee. Measurements have been performed at several depths in the water column (8–14 depths in big lakes, except in Lake Morat −6 depths). A typical example could be Geneva-big: 0–2.5–5– 7.5–10–15–20–30–50–100–200–250–300–309 m. Sampling frequency is variable (biweekly, monthly or seasonal in big lakes and monthly to yearly in small ones). The main lake characteristics are summarized in Table 1. Data for some big lakes and for all small lakes are shown in the Supporting Information file. Most of the OC data corresponds to DOC concentrations but in a few cases POC or total organic carbon (TOC) concentrations were measured instead. This information is given in Table 2. The exact analytical method used (e.g., pore size in filtration, type of OC analyser) were not communicated by FOEN. In the case of Lake Geneva-big, DOC values have been calculated by the difference between TOC and POC concentrations, which probably introduces a non-negligible error. In this lake, strong concentration variations are observed before 1998 (Fig. 2); they are very probably due to analytical problems. For this reason, data before 1998 have not been considered. In big lakes, a parameter called integrated OC has been calculated by: (i) dividing the water column into layers, each layer containing all the concentration data measured (DOC, except Lake Morat, in which only TOC data are available) at equally spaced points, (ii) averaging OC concentrations in each layer to obtain the mass of OC in a column of 1 dm2 of section area and the height of the corresponding layer, (iii) adding all OC masses from surface to bottom. Integrated DOC is then defined as the precedent mass of OC divided by the lake depth in dm; it represents the weighted DOC concentration at the sampling point. We consider these weighted concentrations to be the best representation of overall OC in lakes at the sampling point. It has to be pointed out that integrated OC might show a different evolution over time than the total mass of OC in the whole lake because this depends on lake morphology and time trends in lake surface layers and it is possible that it could be in the opposite direction in the bottom. In smaller, better mixed lakes, OC at each depth has been considered to ascertain time trends without further ‘processing’. In the deepest small lakes (Sempachersee, Hallwilersee and Baldeggersee) integrated OC was calculated but not used (see Section 4). 2.2. Data treatment methods Existence of temporal trends has been assessed by the Seasonal Mann–Kendall (SMK) method (Gibbons and Coleman, 2001), which is

J.C. Rodríguez-Murillo, M. Filella / Science of the Total Environment 520 (2015) 13–22

15

3 27

21 19

16 18 22 14

1 34

8

28 25

36

10 26 31 33 29 32 35 6

2

9 20

7 30 17

11 23

5

12 24

4

15 13

Fig. 1. Situation of the 36 lakes with time series of organic carbon. Red stars are sampling stations in big lakes and blue stars stations in small lakes. 1. Lake Bienne, 2. Lake Constance– Obersee, 3. Lake Constance–Untersee, 4. Lake Geneva-small, 5. Lake Geneva-Big, 6. Lake Lucerne, 7. Lake Morat, 8. Lake Neuchâtel, 9. Lake Zurich, 10. Baldeggersee, 11. Lac de Brenet, 12. Lac de Bret, 13. Lac de Bretaye, 14. Burgäschisee, 15. Lac des Chavonnes, 16. Greifensee, 17. Lac de la Gruyère, 18. Hallwilersee, 19. Hasensee, 20. Hüttnersee, 21. Hüttwilersee, 22. Inkwilersee, 23. Lac de Joux, 24. Lac Lioson, 25. Lützelsee, 26. Mauensee, 27. Nussbaumersee, 28. Pfäffikersee, 29. Rotsee, 30. Schiffenensee, 31. Sempachersee, 32. Soppensee, 33. Steinibühlweiher, 34. Lac des Taillères, 35. Tutenseeli, and 36. Türlersee.

well-suited to dealing with non-normal, seasonal time series. When either seasonality cannot be present or SMK does not work, the Mann– Kendall (MK) method has been used (Gibbons and Coleman, 2001). For this purpose, the necessary functions were implemented in MATLAB. The statistical significance of the trend is obtained in MK methods using the Z-statistic test of the sum of signs of the differences between every pair of values; Z shows a normal distribution and therefore the statistical significance of the temporal trend can be evaluated by the p value. However, the current blind use of p-values is being increasingly challenged (Nuzzo, 2014), particularly in the case of tiny effects (Siontis and Ioannidis, 2011). For these reasons, we have chosen to show all trends, whether significant or not. Sen's slopes have been computed to calculate the strength of the temporal trend (Sen, 1968). LOWESS (Locally Weighted Scatterplot Smoothing) (Cleveland, 1979) has been used to smooth temporal series to facilitate visual comparison. It was calculated with XLSTAT (www.xlstat.com). The order of the polynomial locally fit to each point was set to 1, the weight function was the tricube, and the degree of smoothing 0.5. 2.3. Estimation of autochthonous OC trends The approach followed to estimate changes of autochthonous OC concentrations vs time is described below. DOC in lakes can have autochthonous (DOCau) or allochthonous (DOCall) origins. Thus DOC (for simplicity, in the following, DOC stands for DOC concentrations) in the lake surface (trophic layer), DOCs, and in the lake bottom, DOCb, at each time t, can be considered as being: DOCs ðtÞ ¼ DOCau ðtÞ þ DOCall ðtÞ

ð1Þ

DOCb ðtÞ ¼ DOC′au ðtÞ þ DOCall ðtÞ

ð2Þ

where DOC′au (t) is the fraction of the surface autochthonous production DOC that reaches the bottom. This fraction depends on the OC recycling taking place at the surface of the lake, the mixing regime of the lake and also on the refractory part of DOC formed from primary production (Fry et al., 1996). If we assume that DOCall (t) is the same in the trophic layer and in the lake bottom, the difference between DOC in the trophic layer and in the lake bottom, Diff (t), is given by: Diff ðtÞ ¼ DOCs ðtÞ–DOCb ðtÞ ¼ DOCau ðtÞ–DOC′au ðtÞ:

ð3Þ

This approximation should be considered reasonable because DOCall has a half-life of several years (Hansell et al., 2004; Weyhenmeyer et al., 2012). Mineralization rates in the hypolimnion of several Swiss lakes have been explained by oxidation of settling organic matter (i.e., POC) and simple reduced molecules and ions from the sediments (Müller et al., 2012), which confirms the relative refractoriness of DOCall and illustrates that easily oxidable DOCau is not relevant in the hypolimnion of lakes. We are also implicitly assuming that differences in DOC mineralization of allochthonous OC between the trophic layer and the lake bottom are not important with regard to temporal evolution of Diff (t). Photochemical processes of DOC mineralization exist and could be important (Granéli et al., 1996): in some (shallow) lakes bottom DOC is consistently higher than surface DOC (e.g., Zurich and Bienne), suggesting a preferential destruction of surface DOC that can be even higher than the autochthonous DOC produced by lake primary production. However, a time independent fraction of allochthonous DOC destroyed by photolysis would not change the time trend of autochthonous DOC as given by Diff (t) in Eq. (3). It is also assumed that there is no generation of DOC at the bottom of the lake (i.e., DOC coming from sedimented POC (Fry et al., 1996) is a minor component of the total integrated DOC) (Buffle, 1988). In seasonally stratified lakes, a significant influx of DOCau

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Table 1 Lake studied and their characteristics.a Lake 1 2 3 4 5 6 7 8 9

Big lakes Bienne Constance–Obersee Constance–Untersee Geneva-big Geneva-small Lucerne Morat Neuchâtel Zurich

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Small lakes Baldeggersee Lac Brenet Lac de Bret Lac de Bretaye Burgäschisee Lac des Chavonnes Greifensee Lac de la Gruyèreb Hallwilersee Hasensee Hüttnersee Hüttwilersee Inkwilersee Lac de Joux Lac Lioson Lützelsee Mauensee Nussbaumersee Pfäffikersee Rotsee Schiffenenseeb Sempachersee Soppensee Steinibühlweiher Lac des Taillères Tuetenseeli Türlersee

Altitude/m 429 395 372 434 429.2 429 406

463 1002 673 1780 465 1690 435 677 449 434 658 434 461 1004 1848 500 504 434 537 419 532 505 596 560 1036 610 643

Maximum depth/m

Surface area/km2

Volume/km3

Residence time/yr

74 254 45 309.7 76 214 45 153 136

39.5 473 63 498.9 81.2 113.7 23 215 68.15

1.24 48 0.83 86 3 11.8 0.550 13.8 3.30

1.2 4.3 0.072 11.3

66 18 20 8.5 31 28 32 75 48 5.5 12 15 4.6 33 28 6 7 8.2 35 16 38 86 27

5.22 0.7 0.5

0.174 0.005 0.0045

4.2 0.3 1

0.19 0.0497 8.45 9.60 9.95 0.11 0.17 0.35 0.12 9 0.0662 0.13 0.6 0.25 3.03 0.47 4.25 14.1 0.227 0.0310 0.44 0.0215 0.50

0.0025 0.00577 0.149 0.220 0.286 0.0004 0.0010 0.0027

0.94 – 1.09

8.5 22

0.132 0.00846 0.0020 0.0011 0.0571 0.00381 0.185 0.640 0.0029

3.4 1.6 8.2 1.4

3.9 0.32 0.66 1.93 0.18 0.85 0.93 0.10 0.83 0.47 2.1 0.44 15 4 0.050

0.0065

2.0

a

Sources: Lotter, 1989; Müller et al., 1998; Lods-Crozet et al., 2008; Matzinger et al., 2010; Müller et al., 2012; Naeher et al., 2012; www.vd.ch/themes/environnement/eaux/lacs; www.cipel.org; www.igkb.org. b Artificial lake (reservoir).

into the lake bottom could happen in the annual mixing. This could somewhat distort the temporal series of Diff (t) but most of autochthonous DOC is produced during the stratification period. Allowing for these approximations, calculation of Diff (t) provides an estimation of the temporal evolution of DOCau (t). POC, productivity, and chlorophyll-a data available for lakes Genevabig and Lucerne have been used to assess Diff (t) temporal series in these lakes.

3. Results

Hüttwilersee, Baldeggersee, Hasensee-East, Lützelsee-negative- and Brenet/Joux and Türlersee-positive). As expected, hydrologically connected small lakes show similar OC evolution, (e.g., Joux-Brenet, Baldeggersee-Hallwilersee, HasenseeHüttwilersee). Most, but not all, nearby lakes show similar time trends (e.g., Nussbaumersee and Hasensee-Hüttwilersee, Burgäschisee and Inkwilersee, Mauensee and Steinibühlweiher do, while Bretaye and Chavonnes do not). This is not always the case for connected big lakes (compare Constance–Obersee with Constance–Untersee). No clear geographic trends are apparent in lake OC temporal trends, but around Lake Zurich and in the Jura area, small lakes show increasing OC while small lakes north of Lake Lucerne mainly have decreasing OC concentrations.

3.1. Organic carbon concentration temporal trends 3.2. Autochthonous OC temporal trends Data used for the evaluation of OC trends in big lakes are shown in Fig. 2. Calculated time trends of OC concentrations for all studied lakes are gathered in Table 2. In these lakes, integrated DOC (TOC in Lake Morat) increases in seven cases (four statistically significantly, p b 0.05) and decreases in two cases (none significantly). In the case of small lakes, DOC generally decreases (13 cases out of 18 temporal series, seven statistically significantly) and increases in five cases but none significantly. When all OC data are considered (including DOC, TOC and POC), OC shows increases and decreases (increases in 14 cases, four significantly; decreases in 16, 7 significantly). In all cases, trends are small (b 1% annual change), except in Constance– Obersee, Geneva-big, and in some of the small lakes (Steinibühlweiher,

DOC from primary production, as approximated by Diff (t) (Eq. (3)), increases in five cases in big lakes and decreases in the other four cases, but only significantly in Neuchâtel (increase) and in Constance– Untersee (decrease) (Table 3 and Fig. 3). A similar analysis has been attempted for small, but deep, lakes (Hallwilersee, Baldeggersee, Sempachersee) but the results (not shown) are not consistent (see Section 4). Prior to the calculation of Dif (t), it was tested whether the concentrations used were statistically different. The integrated DOC concentrations between 0 and 30 m and the corresponding DOC concentrations at the bottom of big lakes (TOC in the case of Lake Morat) are significantly different in the nine cases studied (Wilcoxon signed-

J.C. Rodríguez-Murillo, M. Filella / Science of the Total Environment 520 (2015) 13–22

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Table 2 Time trends for the time series of OC concentrations. Slopes are Sen's slopes. NS: non-statistically significant trend (p N 0.05); (*): 0.05 N p N 0.01; (**): 0.01 N p N0.001; (***): p b 0.001. Lake

Period

Type of OC

Mean OC/mg C L−1

OC vs time slope/mg C L−1 yr−1

Variation/% yr−1

Significance

1 2 3 4 5 6 7 8 9

Big lakes Bienne Constance–Obersee Constance–Untersee Geneva-small Geneva-big (from 1998) Lucerne Morat Neuchâtel Zurich

1987–2010 2000–2009 1999–2010 1984–2010 1998–2010 1981–1998 1991–2010 1982–2009 1985–2005

DOC int DOC int DOC int DOC int DOC int DOC int TOC int DOC int DOC int

3.16 1.3 1.4 1 0.94 0.88 3.16 2 1.2

−0.0033 0.022 −0.0053 0.0065 0.012 0.0058 0.0119 0.013 0.00417

−0.17 1.69 −0.38 0.65 1.3 0.66 0.38 0.65 0.35

NS NS NS (***) (**) (**) NS (**) NS

10 11 12 13 14 15 16 17 18

Small lakes Baldeggersee Lac Brenet Lac de Bret Lac de Bretaye Burgäschisee Lac des Chavonnes Greifensee Lac de la Gruyère Hallwilersee

19

Hasensee

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

Hüttnersee Hüttwilersee Inkwilersee Lac de Joux Lac Lioson Lützelsee Mauensee Nussbaumersee Pfäffikersee Rotsee Schiffenenseeb Sempachersee Soppensee Steinibühlweiher Lac des Taillères Tuetenseeli Türlersee

1979–1990 2000–2012 1987–2011 1993–2002 1991–2010 1993–2002 1982–2008 1991–2010 1980–2010 1998–2010 1985–2010 1989–2002 1984–2004 1991–2008 1989–2004 1994–2010 2000–2012 1993–2002 1991–2007 1978–2008 1976–2002 1987–2008 1978–2008 1991–2010 1979–1992 1980–2008 1978–2008 1993–2010 1978–2008 1991–2008

DOC TOC TOC DOC DOC DOC POC TOC DOC TOC POC DOC DOC POC DOC DOC TOC DOC POC DOC DOC POC DOC TOC DOC DOC DOC DOC DOC POC

3.54 3.11 3.39 3.03 8.14 1.74 0.88 1.53 3.26 4 0.77 6.84 7.99 1.68 6.79 5.99 3.51 0.94 2.19 4.79 6.74 0.89 2.19 1.98 3.19 4.73 8.46 5.35 5.14 0.74

−0.1 0.061 0.013 0.029 0.0059 −0.0081 0.0059 −0.006 −0.013 −0.013 0.005 −0.17 −0.05 0.014 −0.2 0.0095 0.054 −0.017 −0.056 −0.019 −0.039 0.0052 −0.0083 0.014 −0.03 0.02 −0.27 0.017 −0.016 0.013

−2.82 1.96 0.38 0.96 0.07 −0.47 0.67 −0.39 −0.4 −0.33 0.65 −2.49 −0.63 0.83 −2.95 0.16 1.54 −1.81 −2.56 −0.4 −0.58 0.58 −0.38 0.71 −0.94 0.42 −3.19 0.32 −0.31 1.76

(*) (***) NS NS NS NS (*) NS (**) NS NS (*) NS NS (**) NS (***) (**) (*) NS NS NS NS NS (*) NS (***) NS NS (*)

rank test, p b 0.01) and bottom concentrations are smaller than 0–30 m concentrations, except in Zurich and Bienne lakes, where the opposite happens.

line with the calculation based on lake bottom concentrations, when account is taken of the approximations made. 3.4. Testing the validity of Diff (t) to represent autochthonous DOC temporal trends

3.3. Allochthonous OC temporal trends If lake bottom DOC is considered to be mostly allochthonous, its temporal trends will give an indication of allochthonous OC variations. Lake bottom DOC increases in all big lakes and sampling points (Table 3 and Fig. 4) even if integrated DOC decreases. However, increases are only significant in three cases (Lucerne and Geneva — both small and big-lake). Zero slopes obtained in some cases are due to the method of calculation (median of slopes between pairs of values) (Sen, 1968) and the fact that many data values have only two significant digits. However, this does not imply a zero trend: Z values of MK analyses are always positive, as are the trends. Temporal increases of lake bottom DOC are small, and fairly similar in lakes Neuchâtel, Lucerne and Geneva-small. They are higher in Constance–Obersee, but the time period under consideration is shorter there, which could exaggerate the trend, as could the existence of multiannual oscillations. In order to circumvent the calculation artefacts mentioned above, the temporal evolution of allochthonous OC has been also calculated as the difference between integrated DOC (t) and Diff (t). When estimated this way, allochthonous OC increases in all the big lakes, except Constance–Untersee, and is zero in Neuchâtel, which is basically in

In a first approximation, autochthonous DOC dynamics should closely follow primary production dynamics. Unfortunately, time series of primary production in lakes are rare. We have been able to collect only two such series in lakes Geneva (Tadonléké, 2010; CIPEL, 2011) and Lucerne (Finger et al., 2013). In Lake Geneva-big (Fig. 3), we have compared each value of annual primary production (PP) with the annual mean of computed Diff (t) in the periods 1988–2009 and 1998–2009. We have chosen to examine also the whole period of DOC data in Geneva-big because Diff (t) data before 1998 are less noisy than DOC data (compare Figs. 2 and 3). A positive relationship exists in both periods between PP and Diff (t), but with no statistical significance. A positive (although non-significant) Diff (t) (p = 0.11) between 1988 and 2010, corresponds to a period with significant increases in both POC (0–30 m) (p = 0.0015), and Chl a (0–30 m) (p = 0.026) (the three temporal trends have been calculated using SMK). Annual increases in POC and Chl a are 0.94% and 0.73% respectively, to be compared with an mean annual increase of 1.2% in Diff (t). In Lake Lucerne, primary production has fallen from 1979 to 1997 (Finger et al., 2013) (MIN-CUA, p b 0.0001) at a rate of 3.8% per year. In parallel, a decrease in Chl a (1977–1992; p = 0.0067, SMK) of 2.5% per year has

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2.5

1.5

2 Lake Geneva-Big

2

Lake Geneva-Small

Lake Lucerne

1.5 1

1.5 1 1

0.5 0.5

0.5

Integrated DOC / mg C L

1

0

1990

1995

2000

2005

2010

2

0

1985

1990

1995

2000

2005

2010

1980

1985

1990

1995

2000

1.5

2 Lake Constance-Obersee

1.5

0

Lake Constance-Untersee

Lake Zurich

1.5 1

1

1

0.5

0.5

0.5

0

2000

2002

2004

2006

2008

2010

0

2000

2002

2004

2006

2008

2010

2.5

4

1985

1990

1995

2000

2005

4

Lake Neuchatel

Lake Bienne 2

3

0

Lake Morat 3

1.5 2

2 1

1 0

1

0.5 1985

1990

1995

2000

2005

2010

0

1985

1990

1995

2000

2005

2010

0

1990

1995

2000

2005

2010

Fig. 2. Time series of integrated DOC in big lakes (TOC in Lake Morat). Measured values (in grey) and LOWESS fitting (in black) are shown.

been observed. Diff (t) has also decreased (1.3% per year) although the decrease is not significant (p = 0.29, SMK). The decrease in Diff (t) is considerably less than the decrease in primary production. Big changes in planktonic communities in Lake Lucerne have been detected and studied (Bürgi et al., 1999), which could explain the differences in behaviour over time between PP and autochthonous DOC and hence Diff (t). These validation exercises illustrate the difficulties often encountered when dealing with production-related data (PP, POC, Chl a). 4. Discussion 4.1. Big lakes Even if the effect is always small and not always statistically significant, the OC trends detected point to an increase in DOC in the lakes studied. Statistically significant DOC increases were also observed in two sampling points downstream of Lake Geneva (Chancy in the

Rhone) and Lake Constance (Rekingen in the Rhine) in a recently published study on OC temporal trends in Swiss rivers (Rodríguez-Murillo et al., 2015). This is in line with the trend observed in the lakes even if in both points about one quarter of the water discharge comes from inflows between the lake outflow and the sampling station (29% in Chancy and 18% in Rekingen) and acknowledging that a decrease is observed in Constance–Untersee (located between Constance–Obersee and Rekingen). This decrease of DOC probably reflects decreasing DOCau in that particular station. Given the number of potential drivers behind trends in OC concentration and the small variations observed, discussion of potential causes would rapidly become purely conjectural. However, the additional information provided by data on allochthonous and autochthonous trends allows further investigation of the causes. The parameter Diff (t) gives an idea of the magnitude of DOCau, especially in deep lakes. Occasionally, peaks of autochthonous DOC are observed but mean values remain small (0.1–0.3 mg C L−1). The evolution

Table 3 Time trends for the time series of Diff (t) and lake bottom DOC concentrations. Slopes are Sen's slopes. NS: non-statistically significant trend (p N 0.05); (*): 0.05 N p N 0.01; (**): 0.01 N p N0.001; (***): p b 0.001. Lake

Bienne Constance–Obersee Constance–Untersee Geneva-small Geneva-big(from 1998) Lucerne Morat (TOC) Neuchâtel Zurich

Diff (t)

Bottom DOC

Allochthonous DOC by difference

OC vs time slope/mg C L−1 yr−1

Significance

OC vs time slope/mg C L−1 yr−1

Significance

OC vs time slope/mg C L−1 yr−1

−0.011 0.0089 −0.0015 −0.00075 −0.0055 −0.0028 0.0075 0.013 0.0028

NS NS (*) NS NS NS NS (***) NS

0 0.025 0 0.0071 0.018 0.0075 0 0.0056 0

NS NS NS (**) (**) (**) NS NS NS

0.0077 0.013 −0.0038 0.0073 0.018 0.086 0.0044 0.0 0.0014

J.C. Rodríguez-Murillo, M. Filella / Science of the Total Environment 520 (2015) 13–22

2

1

0.4

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0

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Diff (t) / mg C L

-0.2

-1

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0 -0.5

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-1

19

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-1

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-0.6

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0

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-2

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Fig. 3. Time series of Diff (t) (autochthonous organic carbon) in big lakes. Measured values (in grey) and LOWESS fitting (in black) are shown.

of autochthonous DOC is in principle related to lake productivity and, at first sight, a decrease should be expected when productivity decreases. After severe eutrophication, lakes in Switzerland have generally been undergoing an intense process of reoligotrophication since the 1980s, with N and specially P decreasing (Jeppesen et al., 2005; Vonlanthen et al., 2012), following the establishment of wastewater treatment plants and enforcement of antipollution regulations. Lake primary production, however, has not followed the same general pattern as nutrients and is steady or still increasing in many places (Liechti, 1994; Gächter and Wehrli, 1998; Tadonléké et al., 2009). Links between reoligotrophication, primary production and OC concentrations appear to depend on the lake and not be linear. Whereas the DOCau level and its evolution over time in particular, depend on the lake (even on the part of the lake we are considering), reflecting the extent to which the lake has recovered from eutrophication, the observed DOCall evolution is similar in all lakes: a slight increase is found, with the exception of in Lake Constance–Untersee. This points to a possible common cause for allochthonous DOC increase in Swiss lakes. Causes can be linked to a modification in input sources and/or to internal lake processes. 4.1.1. Input sources Information about changes in input fluxes into lakes can be obtained from temporal trends in riverine OC concentrations in sampling stations near lake entrances. Weak long term trends in rivers (a general statistically significant decrease in TOC and a less clear increase in DOC concentrations) have been observed (Rodríguez-Murillo et al., 2015). Lake DOCall shows a more uniform trend than riverine DOC. What can also change with time is the type of OC being transported by the rivers (i.e., the more or less refractory character of organic matter). This can lead to changes in OC mineralisation rates; changes

becoming visible in lakes and not in rivers because of the difference in water residence times in both types of system. Changes in OC quality with time have been observed in some rivers (Apsite and Klavins, 1998; Worrall and Burt, 2007; Dawson et al., 2008; Erlandsson et al., 2008; Lepistö et al., 2008). In the case of Swiss riverine waters, no time series of NOM-related parameters, that could provide an appreciation of NOM types (e.g., SUVA, colour, C/N ratio), exist although, as discussed in Rodríguez-Murillo et al., 2015, differences in TOC and DOC temporal trends in rivers point to differences in the time evolution of different types of NOM. The discussion of why NOM composition in rivers might change over time is outside the scope of this study. 4.1.2. Internal lake processes Many possible internal lake causes can be found: (i) a decrease in lake OC mineralization rates associated with lower nutrient concentrations; (ii) an increase in DOC flux from the sediments linked to increased bottom water temperature; (iii) an increase in lake DOC production from incoming riverine POC; and (iv) a decrease in water residence time leading to a higher refractory DOC concentration (Findlay and Sinsabaugh, 1999; Porcal et al., 2009). We have assessed the evolution of the water residence times in the seven big lakes of this study. Lake levels are regulated, and long-term change in the annual mean level is minimal (http://www.hydrodaten.admin.ch/en/index. html); water volume steady state conditions are thus assured. Output flow is the sum of river outflow, evaporation, net anthropic water withdrawal and loss of water to aquifers. River outflow has been computed for the seven big lakes using data from FOEN (http://www. hydrodaten.admin.ch/en/index.html): all river outflows decreased in the last 15–20 years; over the last century mean Swiss evaporation has increased about 125 mm due to increasing temperatures but the contribution of evaporation to the output flow is minor and this increase

20

J.C. Rodríguez-Murillo, M. Filella / Science of the Total Environment 520 (2015) 13–22

2.5

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0

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Fig. 4. Time series of bottom DOC (TOC in Lake Morat) concentrations (allochthonous organic carbon) in big lakes. Measured values (in grey) and LOWESS fitting (in black) are shown.

never compensates the decrease of river outflows; human use of water for several purposes is steady as is the loss of water to aquifers. Therefore, water residence times have increased in all big lakes and cannot explain the observed increase in DOC. Existing data does not allow the role of other possible internal lake causes mentioned above to be determined. An increase in refractory carbon in other temperate lakes, for instance in Lake Biwa (Japan) (Imai et al., 2001), has been reported as has an increase of allochthonous DOC between 1993 and 2009 in five lakes in NE USA (SanClements et al., 2012). However, there is a dearth of studies that take into account the type of organic matter and could allow confirmation of whether this tendency is general. The detection of long-term DOC trends in lakes is complicated by the multi-scale variability of DOC (daily, seasonal, multiannual). Global change signatures require long term data but, even in long term data, DOC variability, in particular multiannual cyclical behaviour of DOC (Filella and Rodríguez-Murillo, 2014), can hide long-term trends. Multiannual cyclic variations of DOC are marked in rivers, rendering the determination of real long-term trends problematic, i.e., fairly different trends could be deduced from DOC data, depending on the beginning and the end of temporal period considered. Such cyclic variations in DOC are damped in lakes, and thus long-term trends could potentially be more easily detected in these systems. In Fig. 2, LOWESS plots reveal the multiannual variability of OC in our big lakes. Only longer OC records could confirm whether this variability is cyclical. Long-term trends could be seen potentially more clearly in bottom data because DOC temporal variability in lake bottom is smaller than in the DOC of the trophic zone. 4.2. The case of small lakes There appears to be a general trend towards decreasing DOC concentrations but it is difficult to reach a firm conclusion. Time periods and

types of OC measured differ. In several cases, artificial aeration and/or oxygenation has been set up (Pfäffikersee, Sempachersee, Baldeggersee, Türlersee, Hallwilersee) (Liechti, 1994) or “hypolimnetic withdrawal” has been applied (Burgäschisee, Mauensee) (Nürnberg, 1987), making it difficult to link observed trends with any ‘natural’ evolution. Any attempt to link the observed trends with possible causes risks being too speculative. Moreover, Diff (t) cannot be used to determine the trend in allochthonous OC. In the majority of small lakes, depth is b 30 m and Eq. (3) is not applicable because the difference between surface layer and bottom DOC is too small. Deeper lake water (Sempachersee, Baldeggersee, Hallwilersee) has been artificially mixed, thereby eliminating the differences between surface and bottom DOCau concentrations (i.e., invalidating Diff (t) as a measure of DOCau). On the other hand, in the absence of restoration practices, the causes for an increase in DOC suggested above for big lakes should also have an impact on the smaller ones. This means that observed DOC temporal trends are the result of a general increase in allochthonous DOC – with increasing soil decomposition more strongly affecting small lakes with significant wetland or peatland in the watershed – and autochthonous DOC whose time trends are heavily dependent on each particular lake. In principle, small lakes should recover faster from eutrophication than big ones because water residence times are (generally) shorter. This suggests that the dominant DOC decrease in those lakes is driven by diminishing productivity, outweighing the probable increase of DOCall. On a lake-by-lake basis, it is difficult to ascertain a trend in primary production using parameters such as POC and Chl a as well as the effect of variations in primary production on DOC concentrations. Lake P loads and P levels have decreased dramatically in most small lakes. But, even when artificial aeration and/or oxygenation has been set up (Türlersee, Pfäffikersee, Sempachersee, Baldeggersee, and Hallwilersee), this has

J.C. Rodríguez-Murillo, M. Filella / Science of the Total Environment 520 (2015) 13–22

not lowered primary production in the lakes (Liechti, 1994; Gächter and Wehrli, 1998). On the other hand, clear decreases of DOC are evident in the three last lakes. No POC data are available for Baldeggersee and Sempachersee, but Gächter and Wehrli (1998) found steady P internal cycling, suggesting steady production in those two lakes. In the DOC time series, a clear decrease is apparent at the time lake correction measures began, followed by levelling off. It is possible that DOC is consumed by increased mineralization but that a new quasi-equilibrium is reached afterwards. On the other hand, POC increases significantly in Türlersee and (non-significantly) in Hallwilersee and Pfäffikersee, but this fact does not necessarily imply an increase in primary production, as this parameter may increase or remain steady even in lakes showing strong recovery from eutrophication (Lake Maggiore, Bertoni et al., 2010). These examples illustrate the extent to which the complex links between reoligotrophication, primary production and OC concentrations make it difficult to explain observed trends in small lakes, and even more difficult to detect possible DOC long-term trends resulting from global change. 5. Summary and conclusions We have compiled time series of OC concentrations for seven big Swiss lakes and another 27 smaller ones and evaluated the temporal trends of these data by using appropriate non-parametric statistical methods. Additionally, we have developed a new approach for constructing time series of autochthonous and allochthonous lake OC concentrations in the seven big lakes by making use of data from different depths. Evaluation of time series of OC concentrations in lakes has proved to be useful in monitoring some of the effects of global change on lakes and their catchments. Our method opens up the possibility of studying both types of lake OC, thereby helping to identify and evaluate the drivers of soil and photosynthetic OC related changes. Organic carbon concentrations generally increase in big lakes and decrease in smaller ones, although in few cases in a statistically significantly way. In big lakes, autochthonous DOC concentrations increase or decrease depending on the lake and the station but allochthonous DOC concentrations generally increase. This behaviour can be explained by the evolution of autochthonous DOC being dependent on the recovery from past eutrophication of each lake and on an increase in the input of (allochthonous) OC from the catchments and/or an increase in the refractory degree of this OC. In small lakes, OC decreasing biological productivity outweighs the probable increase of allochthonous OC in most cases. Acknowledgements We thank Ursula Leuenberger from the Swiss Federal Office for the Environment (FOEN), Pascale Nirel from the Service de l'écologie de l'eau (SECOE) of Canton Geneva (Switzerland) and Stéphan Jacquet from INRA (Thonon, France) for kindly giving us access to data. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.02.085. References Adrian, R., O'Reilly, C.M., Zagarese, H., Baines, S.B., Hessen, D.O., Kellerf, W., Livingstone, D.M., Sommaruga, R., Straile, D., Van Donk, E., Weyhenmeyer, G.A., Winder, M., 2009. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297. Anderson, N.J., Stedmon, C.A., 2007. The effect of evapoconcentration on dissolved organic carbon concentration and quality in lakes of SW Greenland. Freshw. Biol. 52, 280–289. Apsite, E., Klavins, M., 1998. Assessment of the changes of COD and color in rivers of Latvia during the last twenty years. Environ. Int. 24, 637–643.

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