Environmental Monitoring and Assessment

, Volume 184, Issue 8, pp 4999–5008 | Cite as

Trends of natural organic matter concentrations in river waters of Latvia

  • Ilga Kokorite
  • Maris Klavins
  • Valery Rodinov
  • Gunta Springe
Article

Abstract

This study revealed significant (P < 0.1and P < 0.05) increasing trends of total organic carbon (TOC) and water colour in most of the studied Latvian rivers during the last decade. However, over longer time periods, there were pronounced oscillations of TOC concentrations, similar to patterns of long-term changes of river discharge regime. On a yearly basis, there was a positive correlation between parameters of organic matter concentration and discharge in all selected rivers (r S = 0.540–0.803; P < 0.01). The impact of discharge on concentrations of organic matter could be masked by other factors, such as changes in precipitation, biological processes, soil types or land use.

Keywords

TOC Water colour Latvia Discharge Trend analysis 

Introduction

Dissolved organic matter (characterised as total or dissolved organic carbon—TOC or DOC) plays a significant role in the global carbon biogeochemical cycle, influence mineral weathering, nutrient cycling and metal leaching as well as pollutant behaviour and toxicity in waters. Organic matter accumulated in wetlands and soils are an important reserve of carbon; however, human activities (extraction of peat, agricultural activities and land-use changes) as well as global climate change can release stored carbon both as greenhouse gases and dissolved organic substances to surface waters. Considering this, the flows of dissolved organic matter are very important indicators of climate change. Flows of organic matter are influenced by proportion of area covered by wetlands in the catchment, intensity of eutrophication, bedrock geology, intensity of agricultural land use, direct anthropogenic load (also industrial effluents and non-point pollution sources) and other features of the catchment area, and so their character evidently depends on the studied region (Gergel et al. 1999).

Internationally, the role of dissolved organic matter has been analysed in the global biogeochemical carbon cycle, biological processes in hydroecosystems, in reduction of pollutants’ toxicity and flows of DOC (Depetris and Kempe 1993; Pettine et al. 1998; Westerhoff and Anning 2000; Arvola et al. 2004; Evans et al. 2005). However, the indications of organic matter trends are quite contradictory, with most studies showing increasing DOC trends (Evans et al. 2005; Roulet and Moore 2006; Vuorenmaa et al. 2006), but at the same time, significant decreasing trends have been found (Arvola et al. 2004). Clair et al. (2008) found a significant decreasing trend of TOC concentrations during the 1980s, when acid deposition was decreasing rapidly, but no significant changes in TOC concentrations during 1995–2005. Increasing DOC concentrations can be explained by global climate change such as increased atmospheric temperature (which in turn leads to higher microbial degradation rate of organic matter; Fenner et al. 2007; Xiang and Freeman 2009), decrease of acid precipitation or sea salt deposition (Hongve et al. 2004). Results from experimental studies (Freeman et al. 2004; Fenner et al. 2007; Hagedorn and Machwitz 2007) support the hypothesis that elevated atmospheric CO2 concentrations, along with increasing temperatures, can enhance DOC export from catchments due to increased primary production and DOC exudation from decaying plants, as well as due to changes in the composition of organic matter. Changes in hydrological regime such as increased discharge and changes of flow path, as well as impacts of droughts, also have an impact on concentrations and fluxes of dissolved organic carbon from catchments (Clark et al. 2008; Dawson et al. 2008; Jager et al. 2009). Worrall and Burt (2007) examined long-term data on DOC concentrations from 315 monitoring stations in Great Britain and found that, despite dominant increasing trends, DOC concentrations in some rivers in the south-west of the country showed a significant decreasing trend. This reveals the importance of region or even catchment-specific processes, e.g. land-use types, anthropogenic pressure and hydrological peculiarities (Worrall and Burt 2007; Mattsson et al. 2005; Yallop and Clutterbuck 2009). In previous studies of dissolved organic matter in Latvia (during 1977–1995), significant decreasing trends of chemical oxygen demand and water colour were found and explained by decreasing anthropogenic load in catchments (Apsite and Klavins 1998). In the last decades, the environmental situation in Latvia is connected with evident climate change phenomena so the question about the trends of organic carbon and their influencing factors is still open. The aim of this study was to analyse long-term changes of concentration of organic matter in river waters of Latvia and the factors controlling it.

Materials and methods

The study site covers the entire territory of Latvia (Fig. 1). Latvia is located on the north-western part of the East European Plain on the coast of the Baltic Sea and has an area of 64,000 km2. Bedrock is covered by Quaternary deposits consisting of moraine material, limnoglacial or fluvioglacial deposits. The climatic conditions can be characterised as humid with mean annual precipitation of 600–850 mm (Klavins et al. 2002). Due to the influence of cyclones, summer temperatures are slightly lower, but winter temperatures are higher than the average for temperate zones. The mean temperature in January varies from −2.6°C to −6.6°C, and in July from +16.8°C to +17.6°C. The rivers in Latvia have mixed water feeding: rain, snowmelt and groundwater. The river discharge in spring is 45–55% of the total annual discharge, and winter contributes only 15–20% of annual discharge (Klavins et al. 2002).
Fig. 1

Location of monitoring sites (solid inverted triangle): 1 the River Tebra 1.5 km upstream Town Aizpute, 2 the River Irbe at hydrological station Vicaki, 3 the River Venta 0.5 km upstream Town Kuldiga, 4 the Lielupe River 0.5 km upstream the Town Kalnciems, 5 the River Iecava at the river mouth, 6 the River Liela Jugla 0.2 km upstream the Village Zaki, 7 the River Gauja 2 km downstream the Village Carnikava, 8 the River Salaca 0.5 km upstream the Town Salacgrīva, 9 the River Aiviekste 0.2 km upstream the river mouth, 10 the River Daugava 1.0 km upstream the Town Jekabpils, 11 the River Dubna 2.5 km upstream the Town Livani, 12 the River Tulija 0.3 km downstream the Village Zoseni

It has been shown that DOC constitutes 95% of the TOC on average for surface waters, and thus, the TOC values are equivalent to DOC (Vuorenmaa et al. 2006). Data on chemical oxygen demand (COD) (after 2003, TOC is used), water colour, parameters of basic water chemistry and river discharge used in this study were obtained from the Latvian Environment, Geology and Meteorology Centre for the period 1977–2005. Water colour was determined colorimetrically until 1995, but after 1995 spectrophotometrically using the Pt/Co scale. COD was determined by oxidation with K2Cr2O7 and titration with ferrous ammonium sulphate (Standard Methods for Chemical Analysis of Surface Waters 1973). At the same sites, the discharge, temperature and basic chemistry were also measured. Commencing in 2002, COD measurements were replaced by TOC measurements. For 1 year (2003), COD and TOC measurements were run in parallel in all monitoring stations. Since 2003, TOC was measured using a Shimadzu Total Organic Carbon Analyser TOC—V CSN. The relationship between COD and TOC was estimated using a calibration experiment indicating the following relationship between recorded COD and TOC:
$$ {\text{TOC}} = \left( {0.2928 \times {\text{COD}}} \right) + 7.9503{; }{r^2} = 0.611,P < 0.05 $$

A similar approach has been used in other studies (e.g. Hejzlar et al. 2003; Worrall et al. 2003; Worrall and Burt 2007; Erlandsson et al. 2008) to calculate DOC or TOC values from historical data of water colour, CODMn or CODCr.

Long-term changes in river discharge, TOC and water colour were studied using the non-parametric Mann–Kendall test (Hirsh et al. 1982; Hirsh and Slack 1984), which can be applied to data sets with non-normal distributions, missing values or ‘outliers’, and serial character (e.g. seasonal changes). The program MULTIMK/CONDMK was used to detect trends, as it allowed including covariates representing natural fluctuations (e.g. meteorological and hydrological data; Libiseller and Grimvall 2002). A Mann–Kendall test value >1.96 indicates an increasing trend at P < 0.05, but less than −1.96 indicates a decreasing trend (P < 0.05). The Mann–Kendall test was applied to the period 1996–2005 because, for this period, water colour was measured by uniform methods. This avoided the impact of changes in analytical methods on study results.

Results and discussion

TOC concentrations in surface waters of Latvia were comparatively high and were influenced by abundance of wetlands, high degree of eutrophication of lakes, surface leakage of soil humus (more intensive during spring and autumn floods) and impact of wastewater (Fig. 2). Mean values of calculated TOC concentrations varied from 15 mg dm–3 in Rivers Tebra and Venta of the western part of the country to 19 mg dm–3 in the Rivers Lielupe, Dubna and Aiviekste and 21 mg dm–3 in the River Iecava (Fig. 2). Both the spatial and seasonal variabilities of concentrations of organic matter in surface waters of Latvia were comparatively high. The larger proportion of wetlands (Table 1) in the Rivers Aiviekste (3%) and Dubna (4.7%) supports higher TOC concentrations in waters of these rivers. Agricultural areas within the river basin can be a source of increased organic carbon concentrations in surface waters of Latvia (Table 1). Population density (especially compared with Western European countries) is low, and the largest cities are located in the vicinity of river mouths; thus, it can be supposed that the impact of direct human loading on TOC concentrations was comparatively low. Calculated TOC values in our study are comparable to measured TOC concentrations in other catchments of boreal and temperate zones, e.g. in Finnish rivers average TOC concentrations were 5–21 mg dm–3 (Mattsson et al. 2005) and in some rivers in Canada 7.5–17.7 mg dm–3 (Clair et al. 2008).
Fig. 2

Variability of TOC concentration (milligrams per cubic decimetre) in river waters of Latvia (1977–2005)

Table 1

Characteristics of the studied river basins from upstream monitoring stations (source: European Environment Agency 2008)

No. of sampling stationa

River

Catchment area (km2)

River length (km)

Mean long-term discharge (m3 s−1)

Population density (inh. km−2)

Land use (%)

Urban

Wetland

Forest

Agriculture

1

Tebra

71.2

21

1.37

20.01

0.25

0.39

59.76

37.27

2

Irbe

1,920

4

16.2

15.62

0.95

2.94

73.13

19.84

3

Venta

8,321

254

65.47

17.43

0.85

0.92

51.75

45.66

4

Lielupe

16,426

90

97.6

31.43

0.84

1.28

43.95

53.37

5

Iecava

998

155

7.22

36.97

1.07

1.92

59.63

36.72

6

Liela Jugla

663

45

6.31

25.61

0.44

2.49

58.68

38.19

7

Gauja

8,890

438

73.95

20.82

0.86

1.43

58.99

37.86

8

Salaca

3,471

92

21.56

12.49

0.37

4.04

55.98

37.63

9

Aiviekste

9,140

111

60.98

19.83

0.72

3

45.73

47.72

10

Daugava

72,182

843

517

30.98

1.19

3.05

38.25

54.28

11

Dubna

2,462

125

16.49

15.25

0.74

4.7

31.16

59.79

12

Tulija

33.4

12

0.34

13.82

0

0

47.33

52.67

aNo. of sampling station as in Fig. 1

Aquatic chemistry of rivers in Latvia and their discharge experienced significant changes (Figs. 3 and 4; Table 2). In studied rivers, there was evident coherence between the character of changes of TOC and river discharge (Fig. 3). Periodic oscillations of long-term river discharge are common for Latvian rivers and the main frequency of changes of high and low water periods are estimated to be about from 27 to 30 years (Klavins and Rodinov 2008). Erlandsson et al. (2008) detected oscillating patterns of long-term changes of chemical oxygen demand (total length of COD records was 35 years) and discharge in large river basins in Sweden.
Fig. 3

Long-term (1983–2005) changes of the River Daugava discharge, TOC concentrations and water colour in river water (data were smoothed with a 12-month moving average)

Fig. 4

Long-term (1983–2005) changes of River Salaca discharge, TOC concentrations and water colour in river water (data were smoothed with a 12-month moving average)

Table 2

Long-term (1996–2005) Mann–Kendall test statistics of water chemical composition

Monitoring station

TOC

Colour

N-NO 3

P-PO 4 3−

HCO 3

SO 4 2−

Mg2+

Na+

Salaca

2.56*

2.90*

−0.83

−0.51

−0.54

−2.61*

−1.36

−0.38

N = 81

N = 109

N = 109

N = 107

N = 83

N = 83

N = 83

N = 85

Gauja

−0.06

1.97*

−1.20

−0.30

−0.39

−3.02*

−2.19*

0.67

N = 82

N = 111

N = 111

N = 111

N = 92

N = 92

N = 92

N = 92

Daugava

0.87

2.41*

0.11

0.26

2.06*

−1.99*

0.54

1.50

N = 50

N = 74

N = 74

N = 74

N = 63

N = 63

N = 63

N = 63

Aiviekste

1.88**

2.42*

0.55

−1.63

1.14

−1.42

−1.09

−1.28

N = 47

N = 71

N = 71

N = 71

N = 45

N = 45

N = 45

N = 45

Dubna

2.28*

1.53

−1.29

−0.23

1.27

−1.72**

−1.71**

−0.90

N = 47

N = 71

N = 71

N = 71

N = 45

N = 45

N = 45

N = 45

Lielā Jugla

1.28

2.69*

−1.33

−1.34

0.07

−2.76*

−2.00*

−1.50

N = 94

N = 94

N = 94

N = 94

N = 94

N = 94

N = 94

N = 94

Lielupe

1.51

2.65*

−0.39

−1.48

−0.13

−1.83**

−2.20*

−1.59

N = 81

N = 111

N = 110

N = 111

N = 87

N = 86

N = 87

N = 87

Iecava

2.19*

1.61

−0.04

1.17

−1.24

−1.61

−1.64**

−0.47

N = 49

N = 77

N = 77

N = 76

N = 26

N = 18

N = 26

N = 26

Venta

1.38

0.89

−0.73

−1.97*

1.62

−2.42*

−1.81*

0.48

N = 64

N = 91

N = 91

N = 91

N = 74

N = 74

N = 74

N = 74

Irbe

1.93**

2.56*

−2.07*

−0.46

1.26

−3.12*

0.03

0.40

N = 64

N = 93

N = 93

N = 93

N = 84

N = 84

N = 84

N = 84

Tebra

1.34

−0.12

−0.93

−1.64

−1.73**

0.26

−1.85**

−0.52

N = 45

N = 72

N = 70

N = 72

N = 18

N = 18

N = 18

N = 18

TOC changes are analysed for period 1996–2003

N number of observation

*P < 0.05; **P < 0.1

Mann–Kendall tests were applied to detect changes of TOC, water colour and parameters of basic aquatic chemistry during recent decades. All significant trends of TOC and water colour values (P < 0.05 and P < 0.1) showed an increasing trend (Table 2). Interestingly, there were no significant changes of TOC concentrations for rivers with large catchment areas (the Rivers Gauja, Daugava, Lielupe and Venta). Despite the good correlation between TOC and water colour (Table 4), there were some differences between trends of changes of TOC concentration and water colour (Table 2). Water colour had a pronounced trend in 7 of 11 studied rivers, indicating that concentrations of coloured dissolved organic matter increased significantly. Nutrient concentrations as well as bicarbonate and sodium ion concentrations did not have pronounced trends, except for bicarbonate in the River Daugava, phosphates in the River Venta and nitrates in the River Irbe (Table 2). Concentrations of sulphate and magnesium ions show a decreasing trend for the study period 1996–2005.

Hongve et al. (2004) found increases of water colour and acidity, but no significant changes of DOC concentrations in lakes in southern Norway. Researchers have proposed that changing hydrological regime and flow paths can alter chemical properties of organic matter, e.g. increased precipitation can flush out from upper soil horizons more coloured and acidic organic compounds with higher molecular weight (Hongve et al. 2004). The differences in trends of TOC and colour in the present study could also be related to increased atmospheric precipitation in Latvia (Reihan et al. 2007; Jaagus et al. 2010).

To explain changes in organic matter concentration, it is important to consider also trends of changes of acidity (and sulphate and chloride ion concentrations) in atmospheric precipitations (Clair et al. 2008). Aquatic chemistry of rivers in Latvia indicates decreasing concentrations of sulphate and magnesium ions (possible origin is weathering of dolomites—an abundant mineral in Latvia—due to acidic precipitation) in river waters, and these trends could be related to decreased acidity of precipitation in recent decades (Terauda and Nikodemus 2007). Thus, it is highly possible that changes in organic matter concentrations in waters of Latvia were also influenced by changes in acidity of precipitation.

Trends of changes of organic matter concentration indicators (TOC and colour) were evidently greatly influenced by changes in river discharge regime. Thus, both univariate and partial Mann–Kendall tests were applied to selected rivers to estimate the influence of discharge on monotonic long-term trends of TOC and water colour (Fig. 5). There was no significant difference between partial Mann–Kendall test values (corrected for water discharge) and univariate test values; thus, the test statistic values were valid and relatively robust in respect to fluctuations of discharge intensity.
Fig. 5

Comparison of trends of TOC and water colour changes in the Rivers Daugava, Salaca and Venta according to values of univariate and partial Mann–Kendall test statistics (1996–2005)

Trends of changes of organic matter concentration indicators (water colour and TOC) were highly variable within a year and among studied rivers, and had strong seasonal character (Fig. 6). Major increases in both TOC and colour occurred in late-winter–spring (February–April/May) and could be associated with spring floods. There were no statistically significant long-term trends of concentrations of TOC and colour in other seasons. However, at the end of summer and early autumn (August–September), water colour had a slightly increasing trend (possibly due to more intensive decay of organic matter formed in eutrophic lakes common in Latvia and decay of higher plants; however, TOC values had a weak decreasing trend. The differences in the values and trends of parameters describing dissolved organic matter concentrations (i.e. colour and TOC) could also be related to recent changes in agricultural practices and contributions from agricultural areas to the dissolved organic matter pool.
Fig. 6

Mann–Kendall test statistics of water colour and TOC concentration seasonal changes in the Rivers Daugava and Venta (1983–2005)

Balance between allochthonous and autochthonous processes and differing organic matter sources influence seasonal changes of TOC concentration. The highest TOC concentrations were in spring during snowmelt and in late autumn (Fig. 7, Table 3). Correlations between river discharge and TOC and colour were strongest in autumn. In spring, these correlations were slightly weaker, perhaps due to a dilution effect from snowmelt. Relatively weaker links between discharge and TOC and colour were common for summer when the intensity of biological processes was greatest.
Fig. 7

Seasonal changes of TOC and discharge in the Rivers Venta and Gauja (monthly average values for 1977–2005)

Table 3

Spearman’s rho correlation between discharge and TOC and water colour in different seasons (1996–2005)

River

TOC

Colour

Winter

Daugava

0.879**

0.708**

N = 12

N = 17

Salaca

0.664**

0.653**

N = 20

N = 27

Venta

0.354

0.460*

N = 17

N = 22

Gauja

0.541*

0.620**

N = 27

N = 27

Spring

Daugava

0.096

0.158

N = 16

N = 24

Salaca

0.445*

0.578**

N = 21

N = 30

Venta

0.382

0.501**

N = 18

N = 27

Gauja

0.691**

0.492**

N = 21

N = 30

Summer

Daugava

0.519

0.371

N = 11

N = 19

Salaca

−0.076

0.362*

N = 22

N = 30

Venta

0.722**

0.487*

N = 16

N = 24

Gauja

0.821**

0.617**

N = 22

N = 30

Autumn

Daugava

0.900**

0.716**

N = 12

N = 19

Salaca

0.691**

0.659**

N = 21

N = 27

Venta

0.657*

0.564*

N = 14

N = 19

Gauja

0.759**

0.677**

N = 21

N = 27

*P < 0.05; **P < 0.01

On a yearly basis, there were high positive correlations between parameters of dissolved organic matter and discharge in all selected rivers (r S = 0.540–0.803; P < 0.01); however, correlation on a yearly basis was lower than the seasonal correlation. There were only weak negative correlations between parameters of organic matter and water temperature both in all data sets and split into seasonal data. In contradiction to Erlandsson et al. (2008), who found concentration of SO 4 2– to be an important predictor of dissolved organic matter, our data revealed only weak correlations (r S = −0.13 to −0.49); however, there were strong negative correlations with Cl, Na+ and HCO 3 (Table 4).
Table 4

Spearman’s rho correlation coefficients between colour, TOC and water quality parameters in selected rivers of Latvia (1996–2005)

River

Parameter

TOC

Q

Temp

pH

Cl

HCO 3

Na+

Ca2+

SO 4 2−

N-NO 3

Daugava

Colour

0.706**

0.540**

0.125

−0.389**

−0.715**

−0.591**

−0.558**

−0.620**

−0.300*

0.080

TOC

 

0.642**

−0.073

−0.277*

−0.648**

−0.633**

−0.636**

−0.580**

−0.223

0.125

Salaca

Colour

0.671**

0.611**

−0.122

−0.449**

−0.595**

−0.445**

−0.533**

−0.474**

−0.481**

0.278**

TOC

 

0.522**

−0.277*

−0.357**

−0.373**

−0.248

−0.208

−0.292*

−0.172

0.235*

Venta

Colour

0.771**

0.630**

−0.340*

−0.101

−0.517**

−0.381**

−0.600**

−0.227*

−0.164

0.494**

TOC

 

0.641**

−0.338*

0.042

−0.362**

−0.246

−0.437**

−0.172

−0.188

0.404**

Gauja

Colour

0.760**

0.660**

−0.128

−0.412**

−0.767**

−0.787**

−0.671**

−0.775**

−0.490**

0.383**

TOC

 

0.803**

−0.375**

−0.543**

−0.569**

−0.745**

−0.685**

−0.655

−0.134

0.618**

*P < 0.05; **P < 0.01

Changes in TOC seemed coupled to oscillating patterns of long-term changes of river discharge (Figs. 3 and 4), suggesting that natural processes play a significant role in actual flows of organic matter. For example, changes in the hydrological regime and climate can influence both production and leaching of organic matter at levels exceeding the impact of human loading. Typically, there was a positive relationship between estimates of organic matter content and river discharge. The present study revealed a close correlation between river discharge and TOC as well as colour. Thus, the linear trend analysis of organic matter concentration changes did not adequately reveal the character of processes influencing production and decay of organic matter in the aquatic and adjacent environments and factors controlling these processes. River discharge is a major factor influencing water colour and TOC; however, climate change also impacts on organic matter flows through effects on the discharge regime.

There are evidently a multitude of factors affecting concentrations of dissolved organic matter in waters and especially in large river basins. Impact of water discharge on concentrations of dissolved organic matter can be masked by complex factors, including long water flow paths, different land-use types, soil types and climate.

Conclusions

Concentrations of dissolved organic matter, indicated by water colour and total organic carbon concentrations, in surface waters of Latvia had high spatial and temporal variabilities. Concentrations of TOC and values of water colour had an increasing trend during the last decade. River discharge is a major factor influencing dissolved organic matter; however, processes influencing organic matter production and decay determine the seasonal variability of changes. Other factors that possibly affect concentrations of organic matter are the changes in acidity of atmospheric precipitation and the human loading intensity during recent decades.

References

  1. Apsite, E., & Klavins, M. (1998). Assessment of the changes of COD and color in rivers of Latvia during the last twenty years. Environment International, 24(5/6), 637–643.CrossRefGoogle Scholar
  2. Arvola, L., Räike, A., Kortelainen, P., & Järvinen, M. (2004). The effect of climate and landuse on TOC concentrations and loads in Finnish rivers. Boreal Environment Research, 9, 381–387.Google Scholar
  3. Clair, T. A., Dennis, I. F., Vet, R., & Laudon, H. (2008). Long-term trends in catchment organic carbon and nitrogen exports from three acidified catchments in Nova Scotia, Canada. Biogeochemistry, 87, 83–97.CrossRefGoogle Scholar
  4. Clark, J. M., Lane, S. N., Chapman, P. J., & Adamson, J. K. (2008). Link between DOC in near surface peat and stream water in an upland catchment. Science of the Total Environment, 404, 308–315. doi: 10.1016/j.scitotenv.2007.11.002.CrossRefGoogle Scholar
  5. Dawson, J. J. C., Soulsby, C., Tetzlaff, D., Hrachowitz, M., Dunn, S. M., & Malcolm, I. A. (2008). Influence of hydrology and seasonality on DOC exports from three upland catchment. Biogeochemistry, 90, 93–113.CrossRefGoogle Scholar
  6. Depetris, P. J., & Kempe, S. (1993). Carbon dynamics and sources in the Parana River. Limnology and Oceanography, 38(2), 382–395.CrossRefGoogle Scholar
  7. Erlandsson, M., Buffam, I., Fölster, J., Laudon, H., Temnerud, J., Weyhenmeyer, G. A., et al. (2008). Thirty-five years of synchrony in the organic matter concentrations of Swedish rivers explained by variation in flow and sulphate. Global Change Biology, 14, 1–8.CrossRefGoogle Scholar
  8. European Environment Agency (2008) Waterbase rivers http://dataservice.eea.europa.eu/dataservice/metadetails.asp?id=984 Accessed 28 August 2008.
  9. Evans, C. D., Monteith, D. T., & Cooper, D. M. (2005). Long-term increases in surface water dissolved organic carbon: observations, possible causes and environmental impacts. Environmental Pollution, 137, 55–71.CrossRefGoogle Scholar
  10. Fenner, N., Freeman, C., Lock, M. A., Harmens, H., Reynolds, B., & Sparks, T. (2007). Interactions between elevated CO2 and warming could amplify DOC exports from peatland catchments. Environmental Science and Technology, 41, 3146–3152.CrossRefGoogle Scholar
  11. Freeman, C., Fenner, N., Ostle, N. J., Kang, H., Dowrick, D. J., Reynolds, B., et al. (2004). Export of dissolved organic carbon from peatlands under elevated carbon dioxide levels. Nature, 430, 195–198.CrossRefGoogle Scholar
  12. Gergel, S. E., Turner, M. G., & Kratz, T. K. (1999). Dissolved organic carbon as an indicator of the scale of watershed influence on lakes and rivers. Ecological Applications, 9(4), 1377–1390.CrossRefGoogle Scholar
  13. Hagedorn, F., & Machwitz, M. (2007). Controls of dissolved organic matter leaching from forest litter grown under elevated atmospheric CO2. Soil Biology & Biochemistry, 39, 1759–1769.CrossRefGoogle Scholar
  14. Hejzlar, J., Dubrovsky, M., Buchtele, J., & Ružička, M. (2003). The apparent and potential effects of climate change on the inferred concentration of dissolved organic matter in a temperate stream (the Mališe River, South Bohemia). Science of the Total Environment, 310, 143–152.CrossRefGoogle Scholar
  15. Hirsh, R. M., & Slack, J. R. (1984). A nonparametric trend test for seasonal data with serial dependence. Water Resources Research, 20, 727–732.CrossRefGoogle Scholar
  16. Hirsh, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research, 18(1), 107–121.CrossRefGoogle Scholar
  17. Hongve, D., Riise, G., & Kristiansen, J. F. (2004). Increased colour and organic acid concentrations in Norwegian forest lakes and drinking water—a result of increased precipitation? Aquatic Sciences, 66, 231–238.CrossRefGoogle Scholar
  18. Jaagus, J., Briede, A., Rimkus, E., & Remm, K. (2010). Precipitation pattern in the Baltic countries under the influence of large-scale atmospheric circulation and local landscape factors. International Journal of Climatology, 30(5), 705–720.Google Scholar
  19. Jager, D. F., Wilmking, M., & Kukkonen, J. V. K. (2009). The influence of summer seasonal extremes on dissolved organic carbon export from a boreal peatland catchment: evidence from one dry and one wet growing season. Science of the Total Environment, 407, 1373–1382. doi: 10.1016/j.scitotenv.2008.10.005.CrossRefGoogle Scholar
  20. Klavins, M., & Rodinov, V. (2008). Long-term changes of river discharge regime in Latvia. Hydrology Research, 39(2), 133–141.CrossRefGoogle Scholar
  21. Klavins, M., Rodinovs, V., & Kokorite, I. (2002). Chemistry of surface waters in Latvia. Riga: LU.Google Scholar
  22. Libiseller, C., & Grimvall, A. (2002). Performance of partial Mann-Kendall tests for trend detection in the presence of covariates. Environmetrics, 13, 71–84.CrossRefGoogle Scholar
  23. Mattsson, T., Kortelainen, P., & Räike, A. (2005). Export of DOM from boreal catchments: impacts of land use cover and climate. Biogeochemistry, 76, 373–394.CrossRefGoogle Scholar
  24. Pettine, M., Patrolecco, L., Camusso, M., & Crescenzio, S. (1998). Transport of carbon and nitrogen to the Northern Adriatic Sea by the Po River. Estuarine, Coastal and Shelf Science, 46, 127–142.CrossRefGoogle Scholar
  25. Reihan, A., Koltsova, T., Kriauciuniene, J., Lizuma, L., & Meilutyte-Barauskiene, D. (2007). Changes in water discharges of the Baltic states rivers in the 20th century and its relation to climate change. Nordic Hydrology, 38(4/5), 401–412.CrossRefGoogle Scholar
  26. Roulet, N., & Moore, T. R. (2006). Browning the waters. Nature, 414(7117), 283–284.CrossRefGoogle Scholar
  27. Standard Methods for Chemical Analysis of Surface Waters. (1973). Leningrad: Gidromeoizdat (in Russian).Google Scholar
  28. Terauda, E., & Nikodemus, O. (2007). Sulphate and nitrate in precipitation and soil water in pine forests in Latvia. Water, Air and Soil Pollution: Focus, 7, 77–84.CrossRefGoogle Scholar
  29. Vuorenmaa, J., Forsius, M., & Mannio, J. (2006). Increasing trends of total organic carbon concentrations in small forest lakes in Finland from 1987 to 2003. Science of the Total Environment, 365, 47–65.CrossRefGoogle Scholar
  30. Westerhoff, P., & Anning, D. (2000). Concentrations and characteristics of organic carbon in surface water in Arizona: influence of urbanization. Journal of Hydrology, 236, 202–222.CrossRefGoogle Scholar
  31. Worrall, F., & Burt, T. P. (2007). Trends in DOC concentration in Great Britain. Journal of Hydrology, 346, 81–92. doi: 10.1016/j.jhydrol.2007.08.021.CrossRefGoogle Scholar
  32. Worrall, F., Burt, T., & Shedden, R. (2003). Long term records of riverine dissolved organic matter. Biogeochemistry, 64, 165–178.CrossRefGoogle Scholar
  33. Xiang, W., & Freeman, C. (2009). Annual variation of temperature sensitivity of soil organic carbon decomposition in North peatlands: implications for thermal responses of carbon cycling to global warming. Environmental Geology, 58, 499–508.CrossRefGoogle Scholar
  34. Yallop, A. R., & Clutterbuck, B. (2009). Land management as a factor controlling dissolved organic carbon release from upland peat soils 1: spatial variation in DOC productivity. Science of the Total Environment, 407, 3803–3813.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Ilga Kokorite
    • 1
  • Maris Klavins
    • 1
  • Valery Rodinov
    • 1
  • Gunta Springe
    • 1
  1. 1.Department of Environmental ScienceUniversity of LatviaRigaLatvia

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