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Water 2014, 6(5), 1360-1418; doi:10.3390/w6051360

Long-term Trends of Organic Carbon Concentrations in Freshwaters: Strengths and Weaknesses of Existing Evidence
Montserrat Filella 1,2,* and Juan Carlos Rodríguez-Murillo 3
Institute F.-A. Forel, University of Geneva, Route de Suisse 10, Versoix CH-1290, Switzerland
SCHEMA, Rue Principale 92, Rameldange L-6990, Luxembourg
Museo Nacional de Ciencias Naturales, CSIC, Madrid E-28006, Spain
Author to whom correspondence should be addressed; Tel.: +41-223-790-300; Fax: +41-223-790-329.
Received: 26 March 2014; in revised form: 25 April 2014 / Accepted: 4 May 2014 / Published: 20 May 2014


: Many articles published in the last few years start with the assumption that the past decades have seen an increase in dissolved organic carbon (DOC) concentrations in the rivers and lakes of the Northern Hemisphere. This study analyses whether the existing evidence supports this claim. With this aim, we have collected published studies where long series of organic carbon concentrations (i.e., longer than 10 years) were analyzed for existing trends and have carefully evaluated the 63 articles found. Information has been collated in a comprehensive and comparable way, allowing readers to easily access it. The two main aspects considered in our analysis have been the analytical methods used and the data treatment methods applied. Both are sensitive issues because, on the one hand, the difficulties associated with correctly determining organic carbon concentrations in surface waters are well known, while, on the other, dealing with real environmental data (i.e., lack of normality, censoring, missing values, etc.) is an extremely intricate matter. Other issues such as data reporting and the geographical location of the systems studied are also discussed. In conclusion, it is clear that organic carbon concentrations have increased in some surface waters in the Northern Hemisphere since the 1990s. However, due to a lack of data in many parts of the world, it is not known whether this phenomenon is general and, more importantly, in the areas for which such data do exist, the reporting and methodological problems in the published studies prevent any conclusion on the existence of a general temporal behavior of organic carbon from being drawn.
organic carbon; DOC; dissolved organic carbon; temporal trends; time-series analysis; freshwaters; lakes; rivers

1. Introduction

Inland waters (ponds, lakes, wetlands, streams, rivers and reservoirs) occupy only a small fraction of the Earth’s surface but have a disproportionate effect on the global carbon cycle. A large amount of the carbon taken up by terrestrial system ends up in inland waters. The resulting riverine export of terrestrial organic matter to the oceans is a key link between terrestrial and marine parts of the global carbon cycle. While the amount of carbon transported is small compared with the massive fluxes between atmosphere and land and oceans, overall it accounts for about half of the net ecosystem production [1,2]. Moreover, inland waters do not act merely as passive “pipes” for carbon transport; rather they are active components of the carbon cycle because organic carbon (OC) in freshwater bodies can also be buried in sediments or mineralized and released back into the atmosphere as carbon dioxide.

Over the past decades, it has become increasingly accepted that dissolved organic carbon (DOC) concentrations have been increasing in rivers and lakes of the Northern Hemisphere. If confirmed, DOC increase may have significant impacts, not only on the global carbon cycle, but also on freshwater food chains, the quality of drinking water and trace element and organic micropollutant circulation and ecotoxicity. The root causes of this increase remain unclear. Although a few review articles on the subject have been published [3,4,5,6], they largely uncritically accept the universality of DOC increase, collate published results and list suggested causes. They very seldom address issues related to the methodology used, quality of the results, etc. This critical study is an attempt to clarify the situation by analyzing published increasing trends and, in particular, the reliability of the analytical and data treatment methods used and, finally, to evaluate to what extent they are not the result of a belief system generated by a so-called “repetition cascade” (i.e., repetition of claims) [7].

2. Methods

For literature searches we used the ISI Web of Science. Careful reading of published papers led to other references. Data considered in this study were restricted to studies published in peer-reviewed papers. Grey literature (i.e., documentary material that is not commercially published, typical examples being technical reports and conference proceedings) was not included. Because of this choice, some presumably interesting results such as those of Monteith and Evans [8], Skjelkvåle [9], Stoddard et al. [10], Gruau and co-workers on French rivers [11,12] or Zobrist et al. [13] are not included. Although one might argue that taking account of some grey literature might be worthwhile, the facts are that its reliability is always difficult to assess due to the absence of peer-review control and that it is often difficult to access. These reasons finally prevailed. Note, however, that in many cases these data are either totally or partially published later in refereed articles, cases in point being Freeman et al. [14] and Evans et al. [15,16], who refer to Monteith and Evans [8], and Eikebrokk et al. [17] and Skjelkvåle et al. [18], who refer to Skjelkvåle [9].

Articles where changes in OC concentration have been studied over short periods of time (e.g., seasonal studies) or extensive comparisons among freshwater bodies in different climatic zones (e.g., [19]) that do not contain long time-series data have been excluded from this study. Since time scales that are too short do not allow long-term trends to be reliably detected, studies based on less than ≈ 10-year data series have not been included. On the other hand, studies where some surrogate parameter of OC (i.e., color, absorbance, chemical oxygen demand) was measured instead of OC itself have been included. Finally, articles discussing trends in OC fluxes but not in OC concentrations have not been considered.

3. Results and Discussion

Sixty-three articles containing long-term OC concentration series have been identified. A few more studies reiterating previously published data or results (and giving the initial publication as a reference) are cited but not considered in the set. Publication dates span from 1989 to 2012. The key information contained in these studies has been collated in a systematic form in three tables. Table 1 collects information about geographical location, system characteristics, period covered, sampling frequency and data sources. Table 2 shows methodological –both analytical and statistical– information. Table 3 contains the trend results and, in order to facilitate the reading, some key information already provided in Table 1 and Table 2. A significant effort has been made to give all key information in a simple and comparable way but this has not always been possible due to the disparate way in which ancillary and methodological information is sometimes given in the original articles.

3.1. Starting Considerations

3.1.1. Data Quality Traceability

Most of the long-series data come from government surveys and sometimes the published articles do not give either the characteristics of the water systems or the analytical methods used in detail. Where references are given, they sometimes refer to the grey literature, always difficult to access and assess, or to previous articles where the information is not always found. Since, in particular in the case of DOC concentrations, values depend heavily on the analytical procedure applied (see corresponding section), the reliability of the data and of the conclusions reached becomes, in practice, difficult to assess when sampling and methodological information is missing.

A point worth mentioning is that not all studies contain independent data. Rather, sometimes previously processed data are totally or partially reused in later studies. Regrettably, this is not always made clear in the articles in question (e.g., Dillon and co-workers publications on Canadian lakes, Worrall’s on UK freshwaters). In some cases, such as in the many studies published by Worrall and co-workers based on UK data, tracking how the data sets are inter-related from the information given in the articles becomes really tricky. Connections between data sets are mentioned in Table 1 when clearly stated or when deduced after careful reading of the article but we are aware that we have probably not spotted all existing links.

A further factor that makes evaluating studies’ reliability more difficult is the fact that very often non-transformed original data are not shown, even not in graphical form. When shown, it is mentioned in Table 2. Other types of data representation (e.g., month or annual means) are also mentioned.

Table 1. Published studies containing long-term organic carbon concentration temporal trends in freshwaters. System characteristics, sampling details and data sources.
Table 1. Published studies containing long-term organic carbon concentration temporal trends in freshwaters. System characteristics, sampling details and data sources.
Ref. SystemPeriodSampling frequencyData source
Type aNo.CountryDetailsGeneral characteristicsAcid rain recovery? b
[20]river1GermanyRiver Elbe, km: 585–620; number of samples unknown
Location on a map
Freshwater tidal zone; pre-oxygen minimum zone; salinity < 1No1985–20071985–1993: almost monthly
1994–2007: February, May–August, November
[21]lakes30Quebec, CanadaLocated N of the St Lawrence River between Ottawa and Saguenay Rivers in Quebec
Location on a map
Lake surface areas: 0.061–2.02 km2 Max depth: 3–38 m
Water retention time: 0.1–9 y (median: 1.8)
Catchment areas: 0.42–6.96 km2
Yes1989–2006Twice a year in spring and fallAcid Rain Program of Environment Canada
[22]streamnot clearWales, United KingdomUpper Hafren catchment, subcatchment of the Upper River Severn (Plynlimon)
Location on a map
Catchment area: 1.17 km2NM1990–2010WeeklyCentre for Ecology and Hydrology (CEH)
Probably some data already included in [23]
[24]stream1Ontario, CanadaPlastic Lake catchment
Location on a map
Small ephemeral streamYes1987–1994, 1999–2009Not given; probably information in [25]Monitoring program, Ontario Ministry of Environment
[26]rivers11EstoniaLarge: Narva, Suur Emajõgi, Pärnu; small in N Estonia: Kasari, Vihterpalu, Keila, Vääna, Pudisoo, Valgejõgi; small in S Estonia: Väike Emajõgi, Võhandu
Location on a map
Total catchment area: 57,619 km2NMTOC: 1998–2007
COD: 1992–2007
6–12 times a yearEstonian national environmental monitoring programme
[27]lakes91CanadaAtlantic Provinces: Newfoundland (NF) (14 sites), southwestern Nova Scotia (WNS) (45), eastern Nova Scotia (ENS) (23), southwestern New Brunswick (NB) (13)
No list of sites given
Approximate location on a map
YesNF, WNS: 1983–2007
ENS: 1990–2007
NB: 2000–2007
Semi–annually, during spring and fall overturn from May to OctoberEnvironment Canada monitoring at four Canadian Air and Precipitation Monitoring Network (CAPMoN)
[28]lake1SwitzerlandLake MaggioreSubalpine lake, recovered from eutrophic period in the late 1970's
Lake surface area: 212 km2
Max depth: 372 m
No1980–2007Monthly: Nov, Dec, Jan, Feb; fortnightly: other months
[29]stream6United KingdomSouth Pennines: Trout Beck (Moor House)
South Pennines: Lower Laithe, Keighley Moor, Agden, Broomhead, Langsett
Location on a map
Peat–rich catchmentsYesT’ Beck: 1993–2006
L’ Laithe: 1994–2006
K’ Moor: 1979–2006
Agden, Broomhead, Langsett: 1961–2006
T’ Beck: weakly
Others: not clear
T’ Beck: Environmental Change Network (ECN)
Data from T’ Beck already published in [[30,31]
[32]stream1USABear Brook watershed, MaineLow–alkalinity headwater streamYes1988–1989, 1990–1995, 1996–2006Weekly
[33]moorland pools4Nether–landsAchterste Goorven (AG), Groot Huisven, MiddelsteWolfsputven, Schaapsven
Location in a map
No characteristics givenYes1978–2006AG: 4 times/year (every season)
The rest: once every 4 years
[34]lakes55CanadaOntario: Dorset (8), ELA (4), Turkey (TLW) (5); Nova Scotia: Kejimkujik (26), Yarmouth (11)
No list, approximate location on a map
Summary of lake characteristics in the articleYes1981–2003Ontario: 5–24 times a year, from May to October
Nova Scotia: 1 spring, 1 autumn
Different sources
Includes, at least, [35] data
[36]streams2Czech RepublicLysina, Pluhuv Bor
No map
Lysina: acidic, catchment 0.273 km2
Pluhuv Bor: well-buffered catchment 0.216 km2
Czech RepublicOre Mountains (Krušné hory)
List of names, location on a map
Catchments: 8–74 km2Yesreservoirs: 1969–2006
streams: (1969, 1974, 1983)–2006
Median sampling: 34 daysOhre River and Labe River Authorities
[38]streams8FinlandForested headwater catchments, eastern Finland: Murtopuro, Liuhapuro, Suoputo, Kivipuro, Välipuro, Porkkavaara, Kangaslampi, Korsukorpi
No map
Catchments: 0.29–4.94 km2Yes2: 1979–2006
3: 1979–1982, 1996–2005
3: 1992–2006
Variable, described in the article
[39]streams3CanadaStreams: Mersey, Moose Pit Brook, Pine Marten Brook (Southwestern Nova Scotia)
Location on a map
Catchments: 297 km2 (Mersey), 17 km2 (Moose Pit Brook), 1.3 km2 (Pine Marten Brook)YesMersey: 1980–2005
Moose Pit: 1983–2005
Pine Marten: 1991–2005
[40]streams6Scotland, United KingdomLoch Ard (3 sites: Burns 2, 10, 11), Allt a’Mharcaidh, Sourhope (Alderhope and Rowantree Bruns)
Location on a map
Catchments: 0.44–10 km2YesBurn 2: 1989–2002
Burns 10, 11: 1988–2003
Allt a’Mharcaidh: 1987–2002
Sourhope: 1995–2006
At least fortnightlyUK Acid Waters Monitoring Network (AWMN) and Environmental Change Network (ECN)
Loch Ard data in [41,42]
[43]streams7Ontario, CanadaHarp Lake (6 catchments), Plastic Lake (1 catchment)
No map
Headwater catchments: 0.097–1.905 km2Yes1980–2002Weekly or fortnightly, more frequently during periods of high discharge
Total: 1530 (PC), 2200 (HP)
Ontario Ministry of Environment Dorset Environmental Science Centre (DESC)
[44]streams7Ontario, CanadaSame data as [43]
No map
Same data as [43]Yes1980–2002See [43]Same data as [43]
[45]stream1Ontario, CanadaPlastic Lake (PC1)No mapWetland dominated catchment: 0.234 km2Yes1980–2001See [43]This catchment is included in [43]
[46]rivers21SwedenNo list, no mapCatchments: 210–26,800 km2YesTOC: 1987–2004
A, COD: 1970–2004
TOC: not given
A, COD: monthly
Rivers included in national or regional monitoring programs (not detailed)
FinlandValkea–Kotinen, lake and catchment outflow
Location on a map
Headwater catchment: 0.30 km2
Mean depth: 3 m
Volumen: 77,000 m3
Yes1990–2003Not givenLake already studied in [48] , same results
[49]lakes12Ontario, CanadaBoreal Shield lakes: 5 near Sudbury (very acidified), 7 near Dorset (less affected)
Location on a map
Lake area: 0.058–0.936 km2
Max depth: 8.0–38.0 m
YesSudbury: (1981, 1982, 1987)–2003
Dorset: 1978/9–2003
Monthly or more frequently during ice–free season
[50]river1FinlandSimojoki river, Finnish Lapland
Location on a map
Catchment: 3160 km2Yes1962–20051962–1981: 4 samples per year
1982–2005: 10–18 samples per year
Regional environment center
[51]lakes and streams522 (6 regions)North America and northern EuropeNo list, incomplete mapRemote systemsYes1990–2004Not givenData collated from several regional and national monitoring initiatives on acid–sensitive terrain
Probably some data already considered in other studies
[41]streams2Scotland, United KingdomLoch Ard: Burn 10 (0.9 km2) and Burn 11 (1.4 km2)
Location on a map
Small afforested catchmentsYes1983–2006Weekly until 2003, thereafter fortnightlyLoch Ard data in [42]
[52]streams3NorwayBirkenes (B), Storgama (S), Langtjern (L)
No map
Severely acidified systems; forested, undisturbed
Catchment area: 0.41–0.8 km2
Yes1985–2003B: daily
S, L: >1992 weekly; <1992 daily
Norwegian program for monitoring long–range transported air pollutants
[53]lakes and rivers117United KingdomNo list, no map Yes1977–2002HMS data: some weekly, most monthlyHarmonised Monitoring Scheme database (HMS)
198 sites from [54] also considered
USAAdirondack lakes (AL) and Catskill streams (CS), New York
List of systems in a table, location on a map
CS streams chosen in the most sensitive to acidification areas; AL lakes: only drainage lakes with retention time < 6 monthsYes1992–2001Lakes: monthly
Streams: variable
Adirondack lakes: selected from the 52 in the Adirondack Long–Term Monitoring (ALTM)
United KingdomList of sites in a table, no mapLocated in the main acid–sensitive regions of the UK, mostly moorlandYes1988–2003Lakes: quarterly
Streams: monthly
Same data as in [16]
FinlandList of systems in a table, location on a mapSmall forest lakes and forest streams
Lake area: 0.024–1.62 km2
Depth: 4.7–19.5 m
Catchment area: 0.28–4.36 km
Yes1987–2003ICP lakes: 1 sample winter and summer, 2 spring, and fall
IM: 8–12 samples per year
10 lakes Regional Monitoring Network of Lake acidification (RMLA), 3 lakes ICP Integrated Monitoring program (ICP UM)
[31]stream1 (2 sampling sites)United KingdomRiver Tees (Moor House): Trout Beck and Cottage Hill Sike
Location on a map
Blanket peat catchmentNM1994–2001WeeklyTrout Beck data already published in [30]
[56]lakes7Ontario, CanadaDorset region:
7 lakes (Blue Chalk, Chub, Crosson, Dickie, Harp, Plastic, Red Chalk) and their 20 subcatchments
Location on a map
Forested, oligotrophic and mesotrophic lakes
Lake area: 0.3214–0.9360 km2
Mean depth: 7.9–14.2 m
Catchment area: 0.955–5.324 km2
Yes1978–19981 to 4 week intervals
United KingdomList of sites, no mapLocated in the main acid–sensitive regions of the UK, mostly moorlandYes1988–2003Lakes: quarterly
Streams: monthly
UK Acid Waters Monitoring Network (AWMN)
[57]river1 (6 stations)USAHudson River (New York), sampling points: km 146 and 5 stations km 63 to 222
Location on a map
Total catchment: 21,034 km2NM1988–2003Fortnightly (km 146), every 2 months (longitudinal series) in other points
[23]streams3 (6 sampling sites)Wales, United KingdomUpper River Severn catchments (Plynlimon): Upper Hafren, Upper Hore, Lower Hafren, Lower Hore, Nant Tanllwyth, South2Hore
No map
Catchments: 3580 km2 (Hafren), 3172 (Hore), 0.916 (Tanllwyth)Yes(1983, 1984, 1988, 1990, 1991)–2002Weakly or fortnightlyCentre for Ecology and Hydrology (CEH)
[18]sites189Europe and North AmericaEurope: Alps (6), East Central Europe (20), Northern Nordic (7), Southern Nordic (19), UK/Ireland (9), West Central Europe (12)
N. America: Maine/Atlantic Canada (18), Vermont/Quebec (15), Adirondacks (48), Appalachian Plateau (9), Upper Midwest (23), Virginia Blue Ridge (3)
List of sites in [9], approximate location on a map
Regions defined based on similar acid-sensitivity and rates of depositionYes1990–2001VariableInternational Cooperative Programme on Assessment and Monitoring of Acidification of Rivers and Lakes (ICP)
Probably some data already considered in other studies
[58]rivers16FinlandList of rivers in a table, location on a mapVegetation: from boreal taiga to sub–arctic vegetation
Mean annual discharge (m3 s−1): 3 > 100, 5 20–100, 8 < 20
NM1975–2000, shorter for 5 riversMonthlyFinnish Environmental Institute (FEI) or regional environment centres
[59]lake1NorwayLake Elvåga in Østmarka area
Location on a map
Forest area bordering Oslo city district
Lake area: 1 km2
Samples from 40 m depth
YesFrom: – 1976 (color) – 1982 (COD)– 1988 (DOC) to 2002No informationOslo Water and Sewage Works
supply reservoirs
streams and rivers
United KingdomList of sites in a table, location on a mapCatchments: 400 m2–2120 km2Many sites, yesvariable–2000; some from 1962, most 10 years longVariableSites from: Freshwater Laboratory; Scottish EPA; North Pennines; UKAWMN; CEH; Yorkshire Water reservoirs; ECN– Forestry Commission
Data in [37] included
[30]stream1United KingdomRiver Tees (Trout Beck)
Location on a map
Blanket peat catchment
Catchment: 11.4 km2
NM1992–2000WeeklyUK Environmental Change Network (ECN)
[60]rivers2United KingdomRivers Tees (Broken Scar), Coquet (Warkworth)
Location on a map
Rivers draining upland peat, low flood waves (2 days)NMTees: 1970–2000
Coquet: 1962–2001
See [61]Same data as in [61]
[62]lakes52 (48 not limed)USAAdirondack Lakes, New York
List of lakes in a table,
no map
Watersheds largely forested, with hardwood or mixed vegetation
Lake area: 0.008–5.125 km2
Max depth: 1.2–32.0 m
Yes1982–2000 (17 lakes), 1992–2000 (52 lakes)MonthlyAdirondack Long–Term Monitoring (ALTM) program lakes
Some data in [63]
[64]lakes163FinlandNo list, location on a mapForested catchments. All acid sensitive.
Small (median area: 0.1 km2), headwater or seepage lakes
Yes1990–1999Each autumnFinnish acidification monitoring lake network (RMLA)
[65]stream1Czech RepublicMalše River, no mapUpland stream
Catchment area: 438 km2
No1969–2000DailyWaterworks Pořešín (Water Supply and Sewage South Bohemia)
[35]lakes9Ontario, CanadaLakes: Blue Chalk, Chub, Crosson, Dickie, Harp, Heney, Plastic, Red Chalk (two basins), located 150 km N of Toronto
Location on a map
Lake area: 0.13–0.94 km2
Max depth: 6–38 m
Renewal time: 1.1–7.7 y
Catchment area: 0.93–5.89 km2
Yes1978–19985–24 times per year during the ice–free period
[66]lakes705CanadaNo list of sites, incomplete map
Quebec, 33 lakes
Ontario, 662 lakes
YesQuebec: 1990–1997
Ontario: 1990–1999
No informationMany different sources, detailed in the article; most Ontario lakes from the Canadian Wildlife Service (CWS)
[67]lakes8Ontario, CanadaLakes: Bell, David, George, Johnnie, Killarney, Nellie, OSA, Ruth–Roy in Killarney Park, near Sudbury
Location on a map
Strongly affected by acidificationYes1988–2001Annually in midsummerSeveral sources given, not clear which one corresponds to OC data
8 (one with 7 sites)
Scotland, United KingdomLoch Ard: 2 streams: Corrie burn (1 site), Burns (7 sites)
Loch Grannoch: 6 streams and 1 loch
Loch Grannoch catchment area: 15.45 km2YesLoch Ard: 1977–2000
Loch Grannoch: 1978–present
Loch Ard: weekly or fortnightly
Loch Grannoch: variable
[61]rivers3United KingdomRivers Tees (Broken Scar), Wear (Wearhead), Coquet (Warkworth)
Location on a map
Rivers draining upland peatNMTees: 1970–2000
Wear: 1969–1998
Coquet: 1962–2000
Tees: daily
Wear: variable
Coquet: initially daily, then weekly
United KingdomNo list, no mapFreshwater draining upland catchments (peatlands)Yes1989–2000Lakes: quarterly
Streams: monthly
UK Acid Waters Monitoring Network (AWMN)
[68]lakes4Ontario, CanadaLakes Nellie, OSA, George and Bell in Killarney Park
Location on a map
All acidification recovery sites
Lake surface area: 1.885–3.474 km2
Max depth: 26.8–54.9 m
Yes1969–1999Not givenKillarney Park is a Canadian EMAN (Ecological Monitoring and Assessment Network) site
Scotland, United KingdomClassified in four geographical areas
List in a table, location on a map
Moorland and forested sitesYes(1972–1988)–2000Variable, described in detail in the articleFreshwater Laboratory
[70]“ICP Waters” sites98Europe and North AmericaNo list, no mapOnly acidification sensitive sites includedYes1989–1998At least 2 periods per yearInternational Cooperative Programme (ICP) on Assessment and Monitoring of Acidification of Rivers and Lakes
[71]lakes344Scandinavia163 Finland, 100 Norway, 81 Sweden
No list, map
Finland and Norway: headwater or seepage lakes, no pollution
Sweden: forested areas, no pollution
Yes1990–1999Once annually (autumn)National Monitoring Programs of Norway, Finland and Sweden (subset from the 5690 lakes in the Northern European lake survey of 1995)
[72]rivers9LatviaRivers Venta, Tebra, Lielupe, Iecava, Misa, Daugava, Dubna, Gauja, Tuliya
Location on a map
Drainage area: 33–70,600 km2NM1977–1995MonthlyLatvian Hydrometeorological Agency
[73]lakes161Ontario, CanadaSudbury region
No list, no map
Acid stressed lakes
Lake surface area: 0.001–3.50 km2
Depth: 0.6–22.2 m
[74]lakes51 but only 37 used for OC trend analysisQuebec, CanadaN of St. Lawrence River between Ottawa River and Baie Comeau; divided in 7 chemically homogeneous regions
No list, location on a map
Headwater lakes
Lake area: 0.13–0.57 km2
Mean depth: 10.2–28.0 m
Renewal time: 12.7–63.6 months
Values quoted are region means
Yes (those not affected, excluded)(1983, 1986, 1989)–199317: 6 times a year
20: twice a year
14: once a year
Environment Canada
Ontario, CanadaExperimental Lakes Area, lakes number 239, 240, 302S and inflowing streams and lake outflows; NW Ontario
Location on a map
Lake area: 0.543, 0.442, 0.109 km2
Mean depth: 10.9, 6.0, 5.1 m
Renewal time: 4–26, <1–6, 4–12 y
Lake 302S artificially acidified to pH 4.5
Yes239: 1972–1990
240: summers 1972, 1975–1978, 1984–1990; all winters, except 1972–1974 and 1976–1981
302: 1981–1990
Inflow streams: 1970–1990
Lakes: monthly or more frequently during the ice–free season and 2–4 times in winter
Streams: weekly
Experimental Lakes Area (ELA)
[63]lakes17USAAdirondack Lakes, New York
List of lakes in a table, no map
15 drainage lakes, 2 seepage lakes
Lake surface area: 0.01–5.035 km2
Max depth: 4–24 m
Retention time: 0.03–2.5 y
Yes1982–1991MonthlyAdirondack Long–Term Monitoring (ALTM) program lakes
SwedenLake Öjaren
Rivers: Ore Älv, Ljusnan, West Dalälven, Hedströmmen, Alsterälven, Nissan, Lyckebyän
Location on a map
Lake surface area: 20 km2, max depth 9 m
River catchments: 365–8493 km2
YesLake: 1960–1988
River Alsterälven: 1966–1987
Lake: 6 times per year
Rivers: monthly
Surface–water monitoring program of the Swedish Environmental Protection Board
SwedenList of rivers in a table
Location on a map
Lake median size: 2 km2
River drainage area: 25–10,797 km2; mean discharge: 4.0–146.3 m3s−1
NMLakes: 1972–1987
Rivers: 1972–1986 (some 1965)
Lakes: lower than in rivers
Rivers: monthly
– Lakes –Long Term Variation (LLTV)
– Running Waters Data Base (RWDB)
[78]lakes4SwedenLakes Oxsjön (OX), Hammardammen (HA), Innaren (IN), Värmen (VAAll in South Sweden
No map
Forest lakes
Lake surface area: 0.90–16.4 km2
Max depth: 2–19 m
Catchment area: 10.2–200 km2
YesOX: 1967–1982
HA: 1972–1988
IN: 1970’s–1980’s
VA: 1976–1986
OX: 3–4 per year (vegetative season)
HA: daily
IN: 37 times in 1970’s; 25, 1980’s
VA: 4 per year
[79]stream1United KingdomRaw waters arriving at Chellow Heights treatment works, Upper Nidderdale, North Yorkshire
Location on a map
Much of the water from Angram and Scar House reservoirs 1979–1987From daily to less than weeklyYorkshire Water

Notes: a The distinction between streams and rivers is not always well-defined, the denomination used by the authors has been kept; b NM = not mentioned.

Table 2. Published studies containing long-term organic carbon concentration temporal trends in freshwaters. Methodological information.
Table 2. Published studies containing long-term organic carbon concentration temporal trends in freshwaters. Methodological information.
Ref.FiltrationOC quantificationmethodOriginal data plotted?OC range a/ mg C L−1Types of OCLake samplingData transformationStatistical treatment
[20]Filtered (0.45 μm cellulose nitrate filters)TOC, DOC: >1997: HTC (DIN 38409–H3–1) <1997: no documentation
POC: probably by difference
Mean annual [TOC], [POC], [DOC] vs. time shown Mean annual values shown, not clear how many sampling points have been averagedNo mathematical treatment
[21]Not mentionedDOC, UV–persulfate oxidation followed by IRMean [DOC] vs. time shown for all lakesMean range: 2.05–8.38 Integrated 0–5 m samples collected from the middle of the lake; when <1 m deep, drawn 1 m from the bottom to the surfaceNo, original data usedTrends: SMK
Slope: magnitude of linear trend with DETECT software (reference given)
[22]No informationDOC, no informationNoMedian: 2.1 Probably all data usedTrends: Integrated Random Walk analysis
[24]No informationDOC, no informationMean monthly [DOC] vs. time shown Monthly means used in calculations
Discharge–weighted means
Trends: SMK, partial MK with different covariates (rainfall, T, discharge)
Slope: not given, probably Sen
[26]Not mentioned>1998, TOC HTC (ISO 8245)
Correlations TOC–COD established, not given and not used
Data for [COD] in five streams shown
Data available in internet
COD (KMnO4) (ISO 15705) Probably all measured values used in calculationsTrends: MK (for COD only)
[27]Not mentionedTOC, see [39]No Biannual values, treated by geographical zonesTrends: SMK
[28]Filtered (precombusted GF/C filters, Whatman)DOC: HTC (Shimadzu 5000A)
POC: CHN elemental analyzer (Carlo Erba)
Data for [DOC] and [POC] shown One sample at the deepest point
Epilimnion: sampler that collects 5–L integrated samples
Hypolimnion: thermocline (50 m depth) and at 50 m intervals down to the bottom; integrated sample by pooling volumes of each sample proportional to the thickness of the layer
Trends: MK
[29]<1984, unfiltered in Agden, Broomhead, Langsett but A corrected: Atrue=1.06 + 0.63Aapparent [80]Not measured
[DOC] = 0.044*Hazen + 3.89 (r2 = 0.93, p < 0.001; 181 water samples, 2005; [81])
1979–1989, Agden, Broomhead, Langsett, Keighley Moor: A (400 nm)
Hazen = 11.77 × A400 established in Broomhead [80]
No Color (Hazen units)
A (400 nm)
Monthly and annual meansTrends: MK (annual observations); SMK (monthly observations)
Slope: Sen
[32]Not mentionedDOC, not mentionedNo Mean monthly concentrationsTrends: SMK
Slope: Sen
[33]Not mentionedDOC, not mentionedYearly [DOC] median values vs. time shown for one system For one system: yearly median values; normalised by log transformationTrends: Spearman rank correlationstrends
[34]Not mentionedDOC, references givenAnnual [DOC] Z–scores vs. time shown
Lakes in the same zone grouped
Dorset: 1.8–5.1
ELA: 3.0–6.7
TLW: 3.6–4.8
NS: 2.1–16.2
When [DOC] measured for different thermal layers, whole–lake [DOC] calculated by adding up total OC mass in each layer and dividing by lake volume; no information about how many lakes in this situation1– Mean annual ice–free values
2– Z–scores (21–yr mean used)
3– Trends calculated by zones after combination of all lakes within a zone and within temporally coherent zones
Regional and global temporal coherence: Pearson’s correlation coefficient
Trends: visual
Slope: LR
[36]Unfiltered samples
[DOC] = [TOC] since [POC] < 5% [DOC] [82] and “sample inlets of TOC analyzers exclude most particles”
1993–1997: HTC Dohrmann Carbon Analyzer
1998–2004: Shimadzu TOC5000
2005– 2007: Tekmar–Dohrman Apollo 9000
[DOC] vs. time data shownL: 18.8,
PB: 20.2
(mean discharge–weighted [TOC])
Two calculations: one based on weekly samples and one on annual discharge–weighted mean concentrations (based on a Nov–Oct water year)Trends: LR
[37]Unfiltered samplesNot measured
“We refer to COD as the DOM concentrations”
DOC/TOC correlations in [46,65] shown
Median “DOM (measured as COD)” vs. time shownMedian reservoirs: 2.1–6.2
Median streams: 3.4–9
COD (KMnO4)No informationMonthly concentrations used in calculations (probably original data since median sampling period: 34 days)Trends: SMK
Slope: Sen
[38]Not mentioned but probably unfiltered since TOC acronym is used in the paperUV–persulfate oxidation or HTC
Missing data 1978–1991 obtained from COD: [TOC] = 1.1218 + 0.6435 × COD (r2 = 0.93; period and number of points used not given)
Mean annual [TOC] vs. time shown for all systems
Probably wrong units
COD (KMnO4) Mean annual and seasonal [TOC] calculated by averaging weekly or biweekly values
Missing winter data interpolated for annual mean calculation
Trends: SMK
[TOC] = [DOC] based on “our experience in these waters showed that POM < 5%”
<1994, UV–persulfate wet oxidation
>1995: HTC (Shimadzu)
Non HTC values corrected according to [83]
[TOC] vs. time data shown5.4–10.0 Trends: weekly data
Slope: monthly values
Trends: SMK
Slope: not mentioned
[40]Filtered (0.45 μm)DOC, no informationAll stream data plottedSite means: 2.19–11.31 Trends: probably measured [DOC]LR (95%)
Periodicity of the mean monthly [DOC] for each data set was determined by deconstructing the time series using a Discrete Fourier Transform
[43]Not mentionedOxidation: UV in acidic persulfate media; colorimetry with phenolphthalein
Same method as in [56]
Annual volume–weighted monthly averaged [DOC] and Z–scores vs. time shownAverage annual: 2.3–10.7 Monthly volume–weighted [DOC] calculated by dividing total mass exported by total discharge
Z–scores (22–yr mean used)
Trends: partial MK with monthly discharge as covariate
Comparison with average [DOC] 5 first years (1980–1984)
[44]See [43]See [43]Same as in [43] presented otherwiseSee [43] Annual average and annual volume–weighted annual [DOC]Trends: MK
Slope: Sen
[45]Not mentionedSame as [43]NoAverage annual volume–weighted: 9.8 Annual volume–weighted [DOC]; see [43]Same as [43]
[46][TOC], COD: unfiltered samples since “differ by <10%, and usually <5% ([84,85,86])”
A: filtered (0.45 μm)
TOC, oxidative combustion (no details given)
COD and A/TOC correlations shown but not used: [TOC] = 0.51 + 0.84 × COD (r2 = 0.88) [TOC]=3.4 + 2.1 × A (r2 = 0.67)
One LOESS of median Z–scores shownMean TOC range: 2.4–17.1A (420 nm, 5 cm cuvette)
One median (all systems) Z–score for LOWESS (no details about calculation of Z–scores)Slope: Sen but no values given
LOWESS smoothing “for illustrative purposes”, span = 0.75
[47]No informationDOC, no informationLake outflow and catchment outflow [DOC] vs. time data shown: 17 (“the water are humic”) Sampling point not clearProbably annual average usedTrends: MK
Slope: Sen
[49]Unfiltered but Dorset samples: 80 μm meshSame method as in [43,44,45,56]Data for all lakes plotted but probably all are annual mean [DOC] vs. time≤6 Composite samples taken either through the epilimnion and metalimnion or volume–weighted samples accounting for bathymetry taken through the whole water columnAnnual ice–free season averagesTrends: MK
[50]Unfiltered samples
[TOC] = [DOC] because “differences between TOC and DOC are very small [86]”
Not measured
Correlation from [87]: [TOC] = 0.675 × COD + 1.94
[TOC] vs. time data shown COD (KMnO4) Concentrations flow adjusted using rank correlation between mean monthly concentrations and mean monthly flowsTrends: “multivariate extension” of MK, first for each month and then combined 30 point moving average
[51]No informationDOC, no informationNo No information“Sites with median [DOC] < 1 mg L−1 excluded”Trends: MK
Slope: Sen
[41]Filtered (0.45 μm polycarbonate membrane filters)DOC: “OC analyzer by oxidation”[DOC] vs. time data shownMean: B10: 7.3; B11: 11.4 No mathematical treatment
[DOC] = [TOC] because “[TOC] = 90%–95% [DOC]: in these catchments”
TOC, method not givenWeekly [TOC] vs. time data shownMean annual: 11.6 (L), 5.3 (B), 4.9 (S) Samples with [TOC] > 18 not included
Annual mean values weighted by month
Trends: MK, SMK
Slope: Sen
[53]Not mentionedMethods of analysis vary between regions and over the years; none detailed
When [DOC] not available: [DOC] = 0.379 × Color0.83 (n = 477, r2 = 0.72; from 44 sites, used in 2 sites) [DOC] = 10.09 × loge (COD)2 – 7.19 (n = 489, (r2 = 0.47, used in 42 sites)
6–year moving average values vs. time shown for some systems Color (Hazen units)
No informationAll records “corrected to a monthly time step”Trends: SMK
Moving averages shown in two figures but not commented
[55]No informationDOC, no informationNo Lake water collected at the lake outletMonths were chosen as seasonsTrends: SMK
Trend test applied to residuals of a flow–concentration model (hyperbolic or log regression fit)
[15]No informationDOC, no informationMedian [DOC] (for 10 lakes and 8 streams) vs. time shown No information Comparison median [DOC] 5 first years with median [DOC] last 5 years (Mann–Whitney test)
[48]Unfiltered samples
[DOC]=[TOC] since [DOC] = 94% [TOC] in Finnish lakes [86]
<1990: COD used to reconstruct [TOC], no correlation given
>1990: UV persulfate oxidation or HTC
All lake data plottedMean lake value range: 1.5–11.3COD (KMnO4)Middle of the lake, 1m depthTrends in annual and individual months evaluatedTrends: SMK
Slope: Sen
[31]No informationNo information[DOC] vs. time data shown No mathematical treatment
[56]Unfiltered, prefiltered: 80 μm polyester meshDOC: oxidation: UV in acidic persulfate media; colorimetry with phenolphthalein3–year running mean normalised with long–term mean1.80–5.23 No information Calculation of 3–year running means normalised with long–term mean
[16]No informationDOC, no informationAll data plotted No information Trends: SMK
Slope: Sen
[57]Filtered (glass fibber filters; no size given)1988–1993: persulfate digestion (ASTRO 2001)
1994–2003: HTC (Shimadzu 5000)
Intercalibration for 1 year
Mean annual [DOC] vs. time plotted for 3 stations≈4 Trends: probably LR
[23]GFC filters (filtered in the field)HTC (TOCsin II Aqueous Carbon Analyzer)[DOC] vs. time data shown for 3 streamsMoorlands and forest: 1.5 (up to 14 in small streams) Raw data and residuals after flow and season filterSlope: LR, SMK (probably Sen)
[18]cNo information, probably in [9]No information, probably in [9]No No information Trends: calculation of confidence limits about the median value in the slope distribution and testing for zero inclusion
Slope: LR
[58]Not mentionedTOC, oxidation to CO2 and IR detection (no details on type of oxidation)No Trends studied in March, May, August and OctoberTrends: SMK
[59]Not mentioned>1986: UV–persulfate oxidation (Astro 1859)
>1996: HTC (Astro 2100)
>1999: HTC (Shimadzu 5000)
Color, [COD] and [DOC] vs. time data shown for a water treatment plant inlet Color (Pt units)
40 m depth Apparently, no mathematical treatment
[47]No informationDOC, no informationProbably monthly mean data, not original data, for Great Dun Fell, Upper Hafron, Warkworth No informationAll monitoring records converted to a monthly time step; then annual average [DOC]Trends: SMK
Slope: probably Sen
[30]No informationDOC, no informationWeekly [DOC] vs. time data shown for the Moor House catchment outlet Trends: SMK
[60]See [61]See [61]Monthly average color vs. time shown for Coquet and Tees (in this case, same figure as in [61]).
Annual median data also shown
See [61] Trends: SMK
[62]No informationDOC, no information[DOC] vs. time data shown for Big Moose38 lakes < 500 μM No information Trends: SMK
[64]No informationTOC, no informationNo Samples taken either from the middle of the lake (1 m depth) or at the outlet Trends: MK
Slope: LR
[65]Not mentionedNot measured
Calibration DOC–COD: [DOC] = 1.4 + 0.67 × COD (n = 235, r2 = 0.88, p < 0.001) from measurements 1995–1998 but not used; trend results given in COD values
Monthly mean COD vs. time shown COD (KMnO4) SMK applied to “monthly average blocks of data”Trends: SMK
Slope: Sen
[35]Not mentionedDOC: UV radiation in acid persulfate media; colorimetry with phenolphthaleinAverage [DOC] and Z–scores vs. time for the ice–free season shownMean lake value range: 1.8–5.1 Samples collected at the deepest location in the lake either from the upper 5 m of the water column during the spring and fall overturns or from the entire water column during stratification. In this case, samples collected every 2 m were volume–weighted for each thermal layer and then volume weighted to give a single value for each sampling dateCalculation of: – mean [DOC] of all sampling dates for each ice–free season – mean annual ice–free [DOC] standardised to Z–scores (21–yr mean used)No mathematical treatment; visual inspection figure
[66]No informationDOC, no informationNo “Non–parametric test procedures” considering seasonality and autocorrelation
[67]No informationDOC, no informationNo Probably original (annual) data used in the calculationsTrends: SMK (autocorrelation considered)
[41]No informationDOC, no information
Correlation DOC–A: [DOC] = 0.58 + 16.4 × A (r2 = 0.89, n = 586) for one stream (same figure) but never used
[DOC] vs. time data shown only for one stream in Loch Ard area1.3–36.8A (250 nm)No information Trends: SMK and permutation based LR
[61]No filtrationNot measured
Calibration DOC–color: [DOC] = 1.09 + 0.051 × Color from measurements 20 June 2000 but not used; trend results based on color values
Monthly average water color for River Tees and annual average color vs. time for all rivers shown Color (Hazen units) Probably monthly average values usedTrends: visual
Slope: LR
[14]No informationDOC, no informationMedian [DOC] Z–scores for lakes and rivers vs. time shown No informationZ–score calculated from quarterly data for lakes and monthly data for riversTrends: SMK
[68]Not measuredData shown for 4 lakes Secchi disk depth LR
[69]No informationDOC, no informationNo No information Trends: SMK and permutation based LR
[70]No informationDOC, no informationNo No information Trends: SMK (autocorrelation considered)
Slope: Sen
[71]Not mentionedTOC, no informationNoColored dots on a map (0.1–100 scale) Norway, Finland: sampling at the outlet after autumn circulation period; Sweden: “sampled in the middle of the lake”Probably original (annual) data used in the calculationsTrends: MK
Slope: Sen
[72]Not mentionedNot measuredColor data for River Lielupe and [COD] data for River Gauja shown Color (Pt scale)
COD (K2Cr2O7)
Original (monthly) data used in the calculationsTrends: MK, SMK
Slope: Sen
[73]Not mentionedDOC, no informationNoDOC range: 0.53–16.70 No information Trends: MK
Slope: LR
[74]Not mentionedDOC: “determined by autoanalyzer”[DOC] vs. time data shown for Truite Rouge and Eclair lakesRange of region’ means: 253.1–564.5 μM Integrated 0–5 m lakewater samples collected with a sampling iron Trends: MK, SMK, Spearman /Lettenmaier, Hirsch and Slack tests (autocorrelation considered)
Slope: LR
[75]Filtered (precombusted Whatman GF/F filters)DOC digestion: acid persulfate by autoclaving (1971–75), UV irradiation (1975–85), heating to 102 °C (>1986)
CO2 measurement: GC: thermal conductivity detector (1971–75), specific conductance after Ba stripping (1976–85), IR (>1986)
Method changes intercalibrated; no details
Mean annual [DOC] vs. time shown for all lakes Measurements “at several depths in the water column of each lake”; no information about treatment of these values No mathematical treatment; probably visual inspection of figure
[63]Not mentionedDOC: UV persulfate oxidation, CO2 detection by IR[DOC] vs. time data shown for Constable and Arbutus pondsMean lake value range: 192–1132 μMSampling at the outlet of drainage lakes (15) and at the surface of seepage lakes (2)Original (monthly) data used in the calculationsTrends: SMK (autocorrelation considered)
Slope: Sen
[76]Filtered (0.45 μm)Not measuredA (Alsterälven) and color (Lake Öjaren) annual means vs. time shown Rivers: A (420 nm, 5 cm cuvette)
Lake: color (Pt scale)
No information Visual
[77]Rivers: filtered (0.45 μm)
Lakes: no information
Not measuredA vs. time shown for River Botorpsström and A annual means vs. time for rivers Botorpsström and Ätran Rivers: A (420 nm, 5 cm cuvette)
Lakes: color (Pt scale)
No information Value comparison
[78]Not mentionedNot measuredColor annual means vs. time for lakes Oxsjön and Hammardammen shown Color (Pt scale)No information Comparison of values from initial and final years
[79]Filtered (0.45 μm Millipore membrane filters)Not measuredA monthly mean averages and12–month running means vs. time shown A (400 nm) Original data averaged to monthly values12–month running means

Note: a All OC concentration values in the table are in mg C L−1 except when stated otherwise.

Table 3. Published long-term temporal trends in organic carbon concentrations in freshwaters.a
Table 3. Published long-term temporal trends in organic carbon concentrations in freshwaters.a
Ref.Type of systemNo.Measured parameter bTemporal trend? cTrend magnitude d/ mgC L−1 y−1CommentsPeriodLocation
[20]river1TOC, DOCDOC “decreased from around 1000 to around 500 μmol L−1” in the late 1980s, constant after 1996; no stats-Relative % of DOC and POC changed1985–2007Germany
[21]lakes30DOC22: ss increasing (p < 0.05)
8: no trend
Range: 0.01–0.14 (individual values given)Trend not related to initial (1989) or mean [DOC]1989–2006Quebec, Canada
[22]streamnot clearDOCIncrease, no stats- 1990–2010Wales, United Kingdom
[24]stream1DOCss increase (p = 0.023)0.02 1987–1994, 1999–2009Ontario, Canada
[26]rivers11COD5 (out of 6 small rivers) N Estonia: ss increase (at least p < 0.05)
Pärnu: increase (0.05 < p < 0.1)
4 S Estonia: no trend
-COD slopes1992–2007Estonia
[27]lakes91TOCss increase: p > 99%: NB (n = 13) (2000–2007) p 95%: NF (n = 14) (2000–2007), WNS (n = 45) (1983–2007, 1990–2007, 2000–2007), ENS (n = 23) (2000–2007) p 90%: NF (1983–2007)
no trend: NF (1990–2007), ENS (1990–2007)
- Newfoundland (NF), W Nova Scotia (WNS): 1983–2007
E Nova Scotia (ENS): 1990–2007
New Brunswick (NB): 2000–2007
[28]lake1DOC, POCDOC: ss decrease, epilimnion and hypolimnion (p < 0.0001)
POC: no trends
-[DOC] halved in 20 y (from 119 to 57 μmol L−1) but it was discontinuous (peaks 1996–1999)1980–2007Switzerland
[29]streams6DOC estimated from color and A (400 nm)ss increases, p < 0.001Trout Beck: 0.06
L. Laithe: 0.09
K. Moor: 0.32
Agden: 0.20
B’head: 0.30
Langsett: 0.33
Authors qualify this DOC as “humic DOC”T’ Beck: 1993–2006
L’ Laithe: 1994–2006
K’ Moor: 1979–2006
Agden, Broomhead, Langsett: 1961–2006
United Kingdom
[32]stream1DOC“DOC concentrations have not varied substantially or systematically”- 1988–2006USA
[33]moorland ponds4DOCAchterste Goorven: increase, p < 0.05 (n = 29)
Schaapsven: increase, p < 0.01 (n = 8)
Groot Huisven, Wolfsputven: no trends
- 1978–2006Netherlands
[34]lakes55DOCCyclic pattern: decrease, increase, decrease
Only one region with ss increase: ELA (4 lakes) (p = 0.015)
ELA: 0.03Synchronous within regions, not synchronous across regions except in Nova Scotia1981–2003Ontario, Canada
[36]streams2TOCss increase (p < 0.001)Weekly samples: Lysina: 0.42, Pluhuv Bor: 0.43
Annual discharge–weighted mean concentrations: Lysina: 0.62, Pluhuv Bor: 0.93
Lysina: 64% increase, Pluhuv Bor: 65%, taking as reference mean 1993–19941993–2007Czech Republic
CODReservoirs: 5 ss increase, (p < 0.001, except Karmenicka: p < 0.01), 2 no trend
Streams: all ss inc
rease, (p < 0.001, except Cerna voda: p < 0.05)
-COD slopes
COD increase positively correlated with average [COD] (R2 = 0.79, p < 0.001)
reservoirs: 1969–2006
streams: (1969, 1974, 1983)–2006
Czech Republic
[38]streams8TOC, 1978–1991 estimated from COD7: increase (p < 0.05)
1: no trend
-Annual trends became detectable when there was at least one season with ss increase2: 1979–2006
3: 1979–1982, 1996–2005
3: 1992–2006
[39]streams3TOCUncorrected. Mersey (1980–2005), Moose Pit (1983–2005), Pine Marten (1991–2005): no trend
Uncorrected. Mersey (1980–1994): ss decrease (p = 0.06), Moose Pit (1983–1994): ss decrease (p = 0.05)
Uncorrected. Mersey, Moose Pit, Pine Marten (1995–2005): no trend
Corrected. Mersey (1980–2005): ss decrease (p = 0.04), Moose Pit (1983–2005): ss decrease (p = 0.008), Pine Marten (1991–2005): no trend
Corrected (all period): Mersey: −0.1, Moose Pit: −0.25
Uncorrected (<1994): Mersey: −0.25, Moose Pit: −0.58
Values < 1994 corrected for differences in OC method responseMersey: 1980–2005
Moose Pit: 1983–2005
Pine Marten: 1991–2005
[40] estreams6DOCLoch Ard (3), Allt a’Mharcaidh (1): increase (p < 0.001)
Sourhope (2): no trend
Loch Ard, Burn 2: 0.28
Loch Ard, Burn 10: 0.22
Loch Ard, Burn 11: 0.79
Allt a’Mharcaidh: 0.15
Marked seasonal pattern (particularly in Loch Ard), with an increasing amplitude in latter yearsBurn 2: 1989–2002
Burn 10, 11: 1988–2003
Allt a’Mharcaidh: 1987–2002
Sourhope: 1995–2006
Scotland, United Kingdom
[43]streams7DOC6 wetland-dominated streams: ss increase (p < 0.05)
1 upland–dominated stream: no trend
-Wetland-dominated streams: 18%–43% increase, reference mean 1980–1984
Increases mainly due to high concentrations in last 4 years
1980–2001Ontario, Canada
[44]streams7DOC6 wetland-dominated streams: ss increase (HP3, HP5, HP6, PC1: p < 0.01; HP4, HP6A: p < 0.05)
1 upland-dominated system (HP3A): no trend
HP3: 0.12
HP4: 0.046
HP4: 0.15
HP5: 0.10
HP6: 0.10
HP6A: 0.094
PC1: 0.12
Same data as in [43,45]
Different results when using annual average or volume–weighted concentrations. Here volume–weighted shown
1980–2001Ontario, Canada
[45]stream1DOCss increase (p < 0.01)0.12Same data as [43,44]
Varying depending on season
1980–2001Ontario, Canada
[46]rivers21TOC, A, CODTOC increase “smaller (than A, COD) and negligible for some rivers”, no stats
Several periodic reversals in the direction of the trends
-Median annual TOC increase: 0.27%
Simultaneous behavior of TOC, A and COD
Synchronicity among rivers
TOC (21 rivers): 1987–2004
A, COD (28 rivers): 1970–2004
DOCLake: ss increase (p < 0.01)
Stream: no trend
0.19Same system studied in [48] where slope = 0.18 mg C L−1 y−1 for 1987–20031990–2003Finland
[49]lakes12DOCSudbury lakes (5): all ss increase (p < 0.05)
Dorset lakes (7): 3 ss increase (p < 0.05)
- Sudbury: (1981, 1982, 1987)–2003
Dorset: 1978/9–2003
Ontario, Canada
[50]river1TOC estimated from COD valuesNo trend (p > 0.05)-DON increased ss (p < 0.01) (1982–2005)
“Increases of DOC occurred earlier (1970s–1980s) but could not be quantified (low sampling frequency)”
[51]lakes and streams522 (6 regions)DOC363: increase, no stats
139: decrease, no stats
“88% of ss trends (p < 0.05) were positive” but nowhere is said how many ss trends found
Values represented in a figure and in histograms per regionUpward ss slopes more frequent below 62° latitude in the UK and in NE USA
Atlantic Canada little evidence of increasing DOC
North America and northern Europe
[41]streams2DOCIncrease, no stats-Increasing amplitude of seasonalvariations leading to a long–term increase1983–2006Scotland, United Kingdom
[52] fstreams3TOCAll ss increase: Langtjern (p < 0.008), Birkenes (p < 0.002), Storgama (p < 0.001)Langtjern: 0.13
Birkenes: 0.06
Storgama: 0.09
All period increases (trend divised by mean TOC): Langtjern: 14%, Birkenes: 22%, Storgama: 36%1985–2003Norway
[53]lakes and rivers117DOC; in some cases, DOC deduced from color (2 sites) or COD values (42 sites)1977–2002 (54 sites): 12 increase, 23 decrease, 19 no trend; no stats
1977–1986 (51 sites): 7 increase, 16 decrease, 27 no trend; no stats
1993–2002 (94 sites): 5 increase, 56 decrease, 33 no trend; no stats
1977–2002: −0.040.02 j
1993–2002: −0.190.08 j
198 sites from [47] also considered1977–2002United Kingdom
DOC75% lakes: ss increase (p < 0.05)
80% streams: ss increase (p < 0.05)
Lake mean: 0.091 g
Stream mean: 0.056 g
Trends nss counted as a trend of 0 when calculating mean trend values1992–2001 USA
DOCIn all: ss increase (Mann–Whitney), most p < 0.001-Average increase last first 5 years compared to 5 last years: lakes: 63%, streams: 71%
Same results as [16] expressed otherwise
1988–2003United Kingdom
TOC, <1990 deduced from COD6 lakes: ss increase (p < 0.001)
3 lakes: ss increase (p < 0.05)
1 lake: ss increase (p < 0.1)
3 lakes: no trends
1 stream: ss increase (p < 0.0001)
Lakes: 0.10, 0.08, 0.14, 0.22, 0.18, 0.03, 0.11, 0.12, 0.04, 0.12
Stream: 0.35
Lakes with ss increase include both clear water and humic lakes
Poor correlation (r = 0.30, p = 0.33) between annual TOC increase and initial [TOC]
[31]stream1 (2 sampling sites)DOCTrout Beck: “almost step change from 1995 to 1997 with little subsequent decline in values”, no stats
Cottage Hill Sike: similar but “with more evidence of a decline after 1997”, no stats
-Trout Beck, same data as in [30] but different conclusions1994–2001United Kingdom
[56]lakes7DOCOscilations (3–year running means)-Oscillations in annual water discharge and total DOC load were similar in the 7 lakes; annual [DOC] variations were similar but less accentuated1978–1998Ontario, Canada
DOCAll sites: ss increases, no statsRange: 0.06–0.51In all sites, annual [DOC] correlated to mean [DOC] for first 5 years (r2 = 0.71)
All period, 91% increase relative to 1988–1993 mean
1988–2003United Kingdom
[57]river1 (6 stations)DOCAll sites: ss increase (p < 0.001)-“DOC concentrations have doubled from 1988 to 2003”
“Net change between 3 and 4 mg C L−1
Decrease in downstream decline
[23]streams3 (6 sampling sites)DOC“significant upwards trend” (p: 0.000–0.023)0.056, 0.058, 0.055, 0.047, 0.051, 0.045, 0.146, 0.055, 0.019Data filtered for season, air T, flow: residual trend for 1983–1993 and levelling off from 1983 onwards (streams draining forest)(1983, 1984, 1988, 1990, 1991)–2002United Kingdom
[18] hsites189 (12 regions)DOC6 regions (n = 121): ss increase (p < 0.05)
4 regions (n = 59): no trends
1 region (Virginia Blue Ridge) (n = 3): ss decrease (p < 0.05)
1 region (Alps) (n = 6): insufficient data
0.05, 0.08, 0.13, 0.06, 0.06, 0.06, −0.04Europe: ss increase in Nordic countries and UK, nss in central Europe
N. America: ss increase in Vermont/Quebec, Adirondacks, Upper Midwest; nss Maine/Atlantic Canada, Appalachian
1990–2001Europe and North America
[58]rivers16TOC10: ss decrease (p < 0.05) at least once during March, May, Aug, Oct-Some ss decrease observed in: 8 rivers only for 1 period, 1 for 2 and 1 for 3; in total: in 13 of the 64 periods considered1975–2000Finland
[59]lake1DOC, color, CODDOC, COD increased since 1990 (no stats)
Color increased 1976–2002 (no stats) but not continuously
-After 2000, color declined (more than 40%), COD and DOC 11%–13%From 1976 (color), 1982 (COD), 1989 (DOC) to 2002Norway
supply reservoirs
streams and rivers
DOC153: ss increase (p < 0.05)
45: no trends
Mean all sites: 0.17 Variable–2000; some from 1962, most 10 years longUnited Kingdom
[30]stream1DOCIncrease, no stats0.62 (annual median increase) 1992–2000United Kingdom
[60]river2ColorIncrease, no stats-Tees median: 1.83 Hazen units y−1 (≈ 0.11 mg C L−1 y−1)
Coquet median: 0.52 Hazen units y−1 (≈ 0.026 mg C L−1 y−1)
Although this study uses exactly the same data set as [61], results for Coquet River differ; no reason given
Tees: 1970–2000
Coquet: 1962–2001
United Kingdom
[62]lakes52DOC1982–2000 (16 not limed): 7 ss increase, 1 ss decrease (p < 0.1)
1992–2000 (48 not limed): 7 ss increase, 41 no trend (p < 0.1)
Mean rate of DOC increase 1982–2000: 0.079 g1982–2000: “the rate of DOC increase more rapid at higher lake [DOC]”1982–2000 (17 lakes)
1992–2000 (52 lakes)
[64]lakes163TOC0%–10% of lakes in different regions: ss increase (p < 0.05)
Most: no trend
Values in a figure 1990–1999Finland
[65]stream1COD1969–2000: ss increase (p < 0.05)
1969–1984: ss decrease (p < 0.01)
1983–2000: ss increase (p < 0.01)
-COD slopes1969–2000Czech Republic
[35]lakes9DOC“Common pattern: concentrations were higher between 1978 and 1982 and from 1990 to 1997”- 1978–1998Ontario, Canada
[66]lakes705DOCQuebec (n = 43): 14% increase, 10% decrease, 76% no trend (p < 0.10)
Ontario (n = 662): 4% increase, 5% decrease, 91% no trend (p < 0.05)
Subset Ontario no CWS (n = 54): 22% increase, 4% decrease, no 74% trend (p < 0.05)
- Quebec: 1990–1997
Ontario: 1990–1999
[67]lakes8DOC1 (Johnnie): ss increase (p < 0.05)
7: no trend
- 1988–2001Ontario, Canada
8 (one with 7 sites)
DOCAll sites: ss increase, no stats- Loch Ard: 1977–2000
Loch Grannoch: 1978–present
Scotland, United Kingdom
[61]rivers3ColorTees: ss increase, no stats
Wear: no trend, no stats
Coquet: ss increase, no stats
-Tees increase: 51 Hazen units (29 years), 1.75 Hazen units y−1 (≈0.1 mg C L−1 y−1)
Coquet increase: 29 Hazen units (39 years), 61%
“Annual averages for the 3 sites show a clear common phase”
Tees: 1970–2000
Wear: 1969–1998
Coquet: 1962–2000
United Kingdom
DOC20: ss increase (p < 0.05)-Annual average increases (5.4%) proportional to mean [DOC] (R2 = 0.81, p < 0.001)1989–2000United Kingdom
[68]lakes4Secchi disk depthNellie, OSA: ss Secchi depth increase (p < 0.05)
George: decrease, no stats
Bell Lake: nss change (p > 0.05)
-George: −0.1 m yr−11969–1999Ontario, Canada
DOC36: ss increase, no statsMedian annual trend values in a figureSite with the lowest DOC, the only one with nss increase
Greatest annual DOC changes in sites with most highly colored waters
(1972–1988)–2000Scotland, United Kingdom
[70]“ICP Waters” sites98DOCNorthern Nordic Countries (n = 6): no trend
Nordic Countries/UK (n = 24): ss increase (p < 0.001)
Central Europe (n = 34): no trend
Eastern North America (n = 22): ss increase (p < 0.01)
Midwestern North America (n = 9): ss increase (p < 0.001)

4.8 × 10−4 i
3.6 × 10−4 i
1.2 × 10−4 i
1989–1996Europe and North America
[71]lakes344TOC42 (12%): ss increase (p < 0.05)
4: ss decrease (p < 0.05)
87%: no trend
Values in a figureLakes with increases located in SE Norway, S Sweden and in a few cases S Finland1990–1999Scandinavia
[72]rivers9Color, CODColor: 4: ss decrease (p < 0.05); 2: ss increase (p < 0.05); 3: no trend
COD: 7: ss decrease (p < 0.05); 1: ss decrease (p < 0.1); 1: no trend
-Color (in Pt scale) and COD slopes1977–1995Latvia
[73]lakes155DOC3%: ss increase, no stats
5%: ss decrease, no stats
92%: no trend
Median: −0.11
Range: −0.75 to 0.42 j
1983–1995Ontario, Canada
[74]lakes37DOC17: ss increase (p = 0.1)
1: ss decrease (p = 0.1)
19: no trend
-Net changes (1985–1993) by region: R1 (n = 6): +1.7 μM C R2 (n = 8): +66.6 μM C R3 (n = 8): +81.6 μM C R4 (n = 5): − R5 (n = 4): − R6 (n = 6): +55.8 μM C(1983, 1986, 1989)–1993Quebec, Canada
DOCLakes: decrease, no stats
Streams: increase, no stats
Lakes: “DOC concentrations declined by 15%–25%”
Streams: “average concentrations increased by 30%–80%”
Early 70's–1990, depending on systemOntario, Canada
[63]lakes17DOC4: ss decrease (p < 0.1)
13: no trend
−0.072 i (Constable),
−0.108 i (Windfall),
−0.144 i (Heart), −0.156 i (Squash)
Lake: color
Rivers: A (420 nm)
Linear decrease from the end of the 1960's to the beginning of the 1970's, followed by an almost linear increase up to 1988 Lake: 1960–1988
River Alsterälven: 1968–1987
Lakes: color
Rivers: A (420 nm)
Lakes: increase, no stats
Rivers: ss increase in 17 (95% level)
Lakes: average increase: 20 mg Pt L−1, marked increase (≈100%) in large areas of N and S Sweden
Rivers: relative increase: 12%–150%; largest increases in the smallest drainage areas; increase appears to be a part of long–term variations (oscillations); no distinct geographical distribution pattern
Lakes: 1972–1987
Rivers: 1972–1986 (some 1965)
[78]lakes4ColorIncrease, no stats Oxsjön: 10 to 20 mg Pt L−1
Hammardammen: <30 to >40
Innaren: 0–10 to 11–20
Värmen: 20 to 50
Oxsjön: 1967–1982
Hammardammen: 1972–1988
Innaren: 1970's–1980's
Värmen: 1976–1986
[79]stream1A (400 nm)No long–term trend, no stats Short term increases in 1980, 1985, 1987
Tendency towards more extreme values
1979–1987United Kingdom

Notes: a Complementary information in Table 1 and Table 2; b In general, the DOC/TOC term used by the authors has been kept except when the term DOC had been used in studies where it is clear that samples were unfiltered; c ss = statistically significant, nss = not statistically significant. Non statistically increases and decreases are considered “no trends”; d Values only given when trend statistically significant, except when mixed in the original publication (in this case, a cautionary note is added). When value in italics, original value in other units; e [88] includes data from two streams (Loch Ard Burn 2 and Allt a’Mharcaidh) but refers to this study for DOC long term trend values; f Data from one of the catchments (Langtjern) further treated in [89] by empirical regression analysis and a process-based model; g Original units: μmol C L−1 y−1; h [17] show exactly the same results. They cite [9] as a source; i Original units: μeq C L−1 y−1; j Probably slopes for non statistically significant trends included.

3.1.2. Limited Geographical Location

Not many long series of reliable OC data exist. An exception is data collected in association with the follow up of a given problem. This is the case of extensive surveys in northern US and European countries related to acid deposition effects. As a consequence, and as Table 1 shows, many of the published studies on OC trends have been obtained in systems affected by acid rain. This means that (i) these systems are located geographically in a limited zone of the planet and that, as a consequence, they are often climatically similar; (ii) the type of organic matter present in the water bodies is similar (i.e., the bodies are mostly rich in humic-type compounds with limited concentrations of other types of organic matter linked to productivity [90,91]). Published studies are distributed as follows: United Kingdom (19, most in Scotland and northern England), Canada (15, mostly in Ontario and Quebec), USA (5, all in the East Coast), Scandinavian countries (13), Czech Republic (3), Estonia (1), Germany (1), Latvia (1), Netherlands (1), Switzerland (1). Three studies [18,51,71] cover a large number of systems over Europa and North America but located in the same climatic zones as the smaller size studies mentioned. Apart from the limited geographical covering of the existing studies, the fact that these OC concentrations have been measured in systems recovering from a strong chemical disturbance such as acid rain means that the main trends observed will be directly related to the chemical changes in water composition associated with the main system modification (either acidification or recovery), eliminating, or at least greatly reducing, any possibility of detecting trends linked to global climatic changes.

3.2. Analytical Aspects

Organic carbon concentration is a particularly “difficult” parameter because it is highly dependent on the measurement method used. For this reason, it is essential to know, and to understand, this aspect of published studies in order to be able to evaluate the validity and meaning of their conclusions. Methodological information concerning analytical aspects is gathered in Table 2. It includes filtration and information about the analytical method used to determine OC or any other surrogate parameter considered.

3.2.1. On TOC, POC and DOC

The simplest classification of the total organic carbon (TOC) pool includes TOC, to be split into particulate organic carbon (POC) and dissolved organic carbon (DOC), both fractions being obtained by filtration through a filter with a nominal pore size of 0.45 (usual in freshwaters) or 0.22 μm (more common in oceanography). Astonishingly, in a majority of the studies (42%–67% of the total) filtration is not even mentioned. This can never be justified. When filtration is mentioned, the pore size (0.45 μm) is given in only six articles and the type of filters without pore size in four further ones. In nine articles it is explicitly said that water was unfiltered and in six of them unfiltered OC is measured and called DOC on the basis that POC only accounts for 5%–10% of TOC in the waters studied. This might be reasonable in humic-type waters, such as the ones existing in higher latitude zones where most of the studies have been performed, but it cannot be assumed in water systems in other climatic zones. The fact that DOC and POC may evolve differently over time [20,28] also needs to be taken into account when considering temporal trends, even in low-POC containing waters.

3.2.2. Organic Carbon Concentration Measurements

OC measurements have never been straightforward. Progress in the measurement of OC concentrations has been led mainly by oceanographers who developed and introduced the high-temperature catalytic oxidation (HTC) method for low OC concentration measurements in the late 1980s [92]. After some problems due to inappropriate blank estimates in the first publications that forced their authors to withdraw their data [93], the HTC technique imposed itself as the technique of choice in the field, for both seawater and freshwater. However, when considering long-term series of OC data, it is unavoidable that a significant part of the data has been obtained by other techniques, mainly through the wet oxidation method (WCO), based on the chemical oxidation of organic compounds by persulfate, combined or not with photo-oxidation using ultraviolet light. What are the implications? First, as pointed out by Dafner and Wangersky [94], when using older DOC data, one should be aware that values are likely to be in the right neighborhood but with a greater variability than we would now accept. A typical range of error of the DOC measurements by a recent HTC apparatus is 1%–2% as a relative value, which corresponds to approximately 1/10 the error of the WCO method [95]. However, what it is less clear is whether both methods, irrespective of their inherent variability, produce (and previously produced!) similar results. To our knowledge, definitive large scale intercomparison tests have never been performed, although different authors conducted comparisons with variable results, ranging from no significant difference [96], low underestimation (3%–6%, [97]) or high underestimation (20%–24%, [83]) by the WCO method. Matters become even more complicated when we consider that, even within a single method, there are response variations over time. For instance, a progressive increase in DOC concentrations has been observed along with the use of progressively stronger oxidants in the WCO method [98].

Another method, used only by Dillon and co-workers [35,43,44,45,49,56] in Canadian lakes employs colorimetry with phenolphthalein as a detection method after UV oxidation of OC to CO2 in acidic persulfate media. How this method compares with the usual infrared detection of CO2 in WCO and HTC methods is unknown.

In the case of the time series considered here, two further questions arise. One, with no clear answer, is whether it is valid to compare results from different studies when obtained using different analytical methods for OC measurement. The second is how the fact of using data obtained with different methods within the same study series is dealt with and what effects this has on the trends reported. Sometimes authors mention that the methods used have been intercalibrated for a period of time (e.g., [39,57,75]) but not all authors report intercalibration (e.g., [36,38,48,59] do not). It is worth mentioning the case of [39] who studied OC trends with and without correcting old wet persulfate data and obtained quite different results (Table 3).

A much more controversial issue is the use of surrogate parameters such as color (expressed in so-called Hazen or Pt units, equivalent to the platinum concentration in a standard solution of platinum/cobalt chloride salts of the same absorbance/color), absorbance (250, 400, or 420 nm) or COD (chemical oxygen demand). This is highly controversial for two reasons. The first one is methodological, concerning how the link is made with “real” OC concentrations. The second reason concerns the validity of the hypothesis underlying all these studies: that OC and the surrogate parameter evolve in the same way with time.

Concerning methodological issues, some studies follow tendencies directly in the surrogate parameter as such [26,60,61,65,72,77] but a common practice is to establish an empirical correlation between the parameter values and OC concentrations using data simultaneously measured over a short period of time [29,38,48,50,53,61] and discuss tendencies, causes, etc. in terms of OC concentrations. Often these correlations are just used to explain the period where OC concentrations have been directly measured (Table 3). When given, correlations used are provided in Table 2. In one case [53], the authors use correlations established in other studies on the assumption that they will also apply to their systems. This approach is difficult to justify. Even extrapolating synchronous data between close systems seems questionable. For instance, Pärn and Mander showed that TOC and COD for rivers in Estonia, a small country, showed different Spearman’s correlation coefficients depending on the area considered (much higher in the North than in the South) [26].

Definitively more open to discussion is the underlying hypothesis that both OC and the measured parameter will change over the years in the same way. Consider, for instance, that Apsite and Klavins [72] showed that statistically significant increasing trends obtained when considering color became statistically significant decreasing trends when considering COD. The surrogate parameters used such as color, absorbance, etc. only respond to a fraction of the organic matter present in the system, usually the fraction more refractory to degradation fraction, often known as humic substances [99,100]. Surprisingly, this fact is rarely acknowledged in the studies considered, with rare exceptions such as [29]. Probably, in many of the systems considered in the studies evaluated here, these types of substances account for the bulk of the OC present (see Section 3.1.2) and, thus, the hypothesis that both change in the same direction, and in the same magnitude, might be applicable. Nevertheless, this remains unproved and the few studies where, directly or indirectly, the question of the quality of the organic matter present has been addressed, do not seem to support this hypothesis. For instance, Dawson et al. [88] showed a significant change in the relationship between UV absorbance and DOC over 22 years at two upland moorland catchments in Scotland; despite increases in long-term DOC concentrations, their analysis suggests that the proportion of hydrophobic material declined. Erlandsson et al. [46] have reported on Swedish rivers where DOC and absorbance (420 nm) were measured between 1987 and 2004 and found that there was a significant increase in the absorbance/DOC ratio in 19 of the 21 rivers considered. They also found an increase in the COD/TOC ratio that corroborated that changes in the quality of the organic matter had occurred. Worrall and Burt [53] examined a seven-year record of daily coagulant/color records in a water treatment plant (Broken Scar, Scotland) and found that the DOC entering the water works was becoming increasingly difficult to remove by coagulation, suggesting that DOC was becoming more hydrophilic in this catchment. They also suggested that there was no reason to believe that the relationship between DOC and color had not shifted over the course of the study; although color showed no significant trend, it is possible that the DOC from the catchment was becoming less colored so that DOC could be increasing without a significant increase in color. Lepistö et al. [50] calculate the C/N ratio (although not directly: OC by assuming a relationship between DOC and COD published in 1993 and organic nitrogen by difference between total and inorganic N) and found that this C/N ratio decreased during the study period (1962–2005), again pointing to a change in the type of organic matter. Temporal changes in the type of organic matter have also been recently shown in stored samples from lakes in the northeastern United States [101].

Finally, it should be added that in about 40% of the studies considered (Table 2), there is no information about the method used for OC quantification and that, rigorously, in these cases it is impossible to go further in the evaluation of the results obtained.

3.2.3. Other Data Quality Issues

Most of the available lake and river time series data are from national and local monitoring programs and it is often not possible to assess the quality of such data on the basis of the contents of the articles (e.g., reproducibility, use of certified reference materials, sampling procedures, etc.). With regard to trace elements, it was shown many years ago that much of the dissolved trace element work published by these programs was incorrect due to problems of contamination during sampling and analysis [102,103,104]. It is unknown whether this type of problem might have also affected OC measurements.

An additional aspect that needs to be considered is that data used in the trend studies are often monitoring data for regulatory purposes and that the fact that the objectives of this type of measurement differ from those of research-oriented studies is not without consequences. For instance, since the main concern of regulatory monitoring is simply to ascertain that some limit values are not exceeded, often not very sensitive techniques are used. The use of censored data (i.e., sets of data where some of the data are known to be “less than” some threshold) always introduces a bias in the magnitude of possible trends observed. The existence of this type of constraint has rarely, if ever, been mentioned in this field.

3.2.4. Bias Towards Studying Systems “Where Something Happens”

In addition to the well-known fact that citation practices confer on negative values an inherent quality of not spreading easily through the literature [7], authors rarely choose to study systems “where nothing happens” and often do not report results where the expected effects are not observed. Obviously, this has the automatic consequence of producing a bias in existing results and in the corresponding accepted belief. Although it is inherent to this type of problem that proving that it exists is well-nigh impossible, it is worth mentioning that it is highly probable that this behavior affects the subject considered here, leading to an overrepresentation of systems showing OC increasing trends.

3.3. Data Treatment Aspects

The second issue that needs to be considered is related to the way experimental data are treated. This information is set out in Table 2. Different aspects need to be discussed: data censoring, data transformation prior to their treatment and methods applied to detect temporal trends and to quantify them.

3.3.1. Data Censoring

It is impossible to know when data has been censored because it is rarely mentioned explicitly in the studies considered (i.e., OC concentration detection limits and the number of values below these). Thus, it is impossible to assess the effect that data censoring might have in the conclusions reached. The only two cases found where explicit mention is made of “manual” censoring practices suggest a lack of understanding of the implications. A highly cited study [51] states: “sites with median concentrations of <1 mg/L were excluded from our analysis” and this in order to restrict their analysis “to sites where DOC concentrations were sufficient to allow reliable quantification of trends”. By doing this, the authors plainly ignored that values ≤ 1 mg C L−1 are common in many systems. It also suggests a bias towards favoring systems with high DOC concentrations, which usually means northern humic-type ones. Since some authors [14,37,48,62,69] observed that trends are correlated with initial concentration levels, it is clear that, in practice, not considering low concentrations introduces a bias towards the measurement of higher trends. On the other end of the spectrum, de Wit et al. [52] wrote: “samples with exceptionally high TOC concentrations (>18 mg C L−1) were excluded from the dataset” because “TOC in these samples had been modified by in-stream processes rather than being products of soil processes”, reasoning difficult to understand when studying OC behavior in natural systems.

3.3.2. Data Transformations

When looking for trends and thus dealing with large data sets, different approaches are possible: use all data in the time series considered, sample a subset of the observations (i.e., plainly eliminate values), use mean values (e.g., monthly, yearly, etc.) calculated from measured ones. Using a subset or averaging is often done simply to ensure some regularity in the temporal distribution of the data in the series (sometimes required for the statistical methods applied). In the case of the time series considered here, the frequency of sampling is highly variable between studies and sometimes even within the same study (Table 1); very often original data are not used when applying data treatment methods but rather monthly or annual means are used instead. Since averaging is not an innocuous procedure [105], the exact procedure used, including sampling frequency and method of calculation of mean values, needs to be described in detail. Unfortunately, this information is very often omitted from the articles. In fact, this is one of the more difficult aspects to trace in the articles considered.

The way mean values are calculated in rivers influences the results obtained as shown by Eimers et al. [44]. These authors compared the effect of using volume-weighted and arithmetic means at seven headwater streams in Canada, obtaining different results. On average, annual measured DOC concentrations were 13%–34% higher than volume-weighted values and, although DOC increases were found in both cases, slopes were much larger in the measured data. Hruška et al. [36] also found differences in the magnitude of the trends observed (but not in the significance level) when using these two types of mean value calculation procedure.

The question merits some further discussion in the case of lakes. Depending on the size of the system (i.e., depth), many boreal and temperate lakes physically stratify in summer. Physical stratification drives chemical stratification and this needs to be taken into account when sampling. Again, in the studies considered here, information about how lake sampling has been performed is sometimes lacking (as is unfortunately the case in the studies covering a high number of systems like in [18,51,71]). When sampling methods are described, different strategies, not necessarily leading to comparable results, have been followed (Table 2). In general, only one value (obtained in very different ways, e.g., sampling at one fixed depth, integrated sampling, value averaging) per date is considered. This strategy simplifies calculations but, firstly, leads to non comparable results and, secondly, when OC is measured at only one point, it does not take into account that OC concentrations may evolve differently in surface waters than at depth and, when averaged or integrated, information is lost while possible existing trends might become less clear.

Data have sometimes been normalized using Z-scores [14,34,35,43,46]. Z-scores are calculated by subtracting from all values the mean over the period under study and dividing by the standard deviation. They have mostly been used for comparing systems with different levels of OC concentrations.

3.3.3. Trend Detection and Quantification

Since many water variables are not normally distributed, it is not generally appropriate to analyze them for temporal trends using parametric methods such as linear regression [106]. Accordingly, non-parametric methods have been largely used in the studies reviewed here. The non-parametric test for trends most frequently applied is the Kendall test (or Mann-Kendall, MK) [107,108]. The MK test compares every pair of values of the variable, and calculates the sign of the difference. The signs (indicating whether the second observation in each pair-wise comparison is higher, lower or equal than the first) for all pair-wise comparisons are summed and a Z-statistic calculated as the sum of signs divided by the standard deviation of the sum of signs. The statistical significance of any trend is indicated by the corresponding p-value. Unfortunately, the significance associated with the detection of a trend is not always given (Table 3).

There are many cases where concentrations in surface waters show strong seasonal patterns. This is often the case of OC. Ideally, seasonal variations must be removed in order to better discern any trend in the studied variable over time. The seasonal Kendall test (SMK) [106] accounts for seasonality by computing the MK test on each of the seasons separately and then combining the results. The SMK has been used in many OC trend studies (Table 2). The existence of seasonality can be tested by applying the Kruskal-Wallis statistic test but authors usually apply SMK without any previous test, presumably deducing seasonality visually or assuming that it probably exists. An alternative way of treating the effect of seasonality is applying trend tests (i.e., MK) to annual mean values. This approach has also been used. Although it eliminates seasonal effects and a part of the random variation of data, it has the drawback of reducing the information content.

The MK test does not estimate the magnitude of trends (slopes) but it has become usual to associate slopes calculated according to the method of Sen [109,110] when a trend is detected. The method of Sen is a nonparametric method where the slope is approximated as the median value of all the pairwise slopes in the time series. However, a non negligible number of studies applied linear regression even after having used the MK method to find the existence of trends.

The MK test is known to be well adapted to censored data (when only one censoring threshold exists; although note that the magnitude of the Sen slope is likely to be in error when using censored data), the presence of outliers and missing values. Limitations of the MK test are that there must be no serial correlation for the resulting p-values to be correct, and that data must be monotonic. A few of authors mention the use of modifications of the MK method that account for autocorrelation [63,67,70,74]; the most usual modification is the one proposed by Hirsch and Slack [111]. It is not excluded that others account for autocorrelation without mentioning it.

Monotonicity is potentially a serious problem in OC trend studies. In practice, monotonicity as such has rarely been statistically tested and data are generally assumed to be monotonic. However, cyclical patterns have been observed by some authors [34,35,46,56,65,77] either by looking at the graphical representation of the data or when applying smoothing methods such as the calculation of moving averages (also called running averages). Although not exempt from problems [105], calculation of moving averages provides a robust description of a data pattern and, although it has been seldom applied to OC data series, where it has been, cyclical patterns have appeared. Cyclical behavior has been observed in many parameters affected by climatic variations [112] and therefore is not astonishing that OC data show it, but it is worrying is that such behavior might have gone undetected in cases where monotonicity has been assumed. Obviously, for cyclicity to be apparent, long temporal series are needed.

It is common in hydrology to attempt to eliminate flow-related variability by adjusting water concentrations to flow (e.g., with LOWESS) and applying a trend test to the residuals. To our knowledge, this procedure has only been used by Burns et al. [55], after hyperbolic or log regression fit of the data. Other authors mention the use of the partial MK test with discharge or other parameters as covariates [24,40].

Some authors estimate the magnitude of the trends by comparing initial and final values either in absolute terms or as a percentage. In principle, this should be avoided because, even if the mean of some initial and final years are used, the value remains very much dependent on initial and final conditions. Sometimes, even increases observed over a given period are extrapolated beyond it (e.g., Worrall et al. [30] found a 53.4% increase over 8 years (1993–2000) but in the abstract talk about a 78% increase since 1970, “the period over which increase has been observed for the catchment as a whole”).

4. Conclusions

Careful analysis of the 63 studies listed in Table 3 has revealed the existence of a number of problems both in the way results have been published (i.e., key information is missing from the articles, making it difficult to judge the reliability of the results; the degree of independence between published studies is fuzzy in some cases) and in the way OC analysis was performed (i.e., different, and not necessarily comparable, methods have been used along the years). In general, data treatment looks to be more problem free from the methodological point of view even if, sometimes, it is difficult to follow some methodological aspects such as, for instance, how authors pass from measured data to the numbers actually used in data treatment. This is not a trivial question because, as Stevenson and co-workers recently showed for temperature [105], different types of climatic patterns and anomalies are captured depending on which of various local and global methods are used. The failure to consider the possible existence of cyclicity, concomitant on the widespread assumption of data monotonicity, may also be a major flaw in OC studies that merits further consideration.

Can the initial question that motivated our study be answered in spite of these limitations? In brief, can we reasonably state that “there is a common trend of increasing concentrations of DOC in streams and lakes”? First, we think that it is clear to any reader that, because of the limited geographical coverage of the existing studies, if any general increasing OC trend did exist, our statement would need to be restricted to some northern zones of the North Hemisphere. Nearly no data exist for many areas of US and Europe temperate zones and none for the rest of the world.

That said, it would nevertheless be tempting to pool together observed trends and “play statistics” with them. However, this would have little sense for many reasons. First, in general: (i) studies cover very different temporal periods; (ii) the number of systems included in each individual study is very different, ranging from one intermittent stream [88] to a major comparison of 705 systems [66]; 33 of the published studies cover less than 10 systems and only 9 more than 100. Curiously, if all trends were pooled together, the existence of four earlier studies [64,66,71,73] covering a huge number of lakes in Canada and Scandinavia (1324 in total), where about 90% of the systems in each study showed no statistically significant trend, would probably give an overwhelming majority for the “non trend” category.

Secondly, how many of the 63 studies contain results that can be considered “usable”? A fast screening shows that not many. If studies that contain the same results published more than once by the same authors are eliminated from the initial 63 studies, 60 remain. Unfortunately, as explained above, there is no way of accounting for the interdependence of other published data. If we consider only studies which contain statistically significant results supported by a p value (irrespective of being increases, decreases or no trends), we are left with 37. And, if we fix the limit of significance at p < 0.05, then only 34 of the initial 63 studies remain. It is worth mentioning here that judging this aspect is sometimes a bit tricky. For instance, in an important study covering 522 systems [51] there is no way of assessing the number of systems with significant trends (except trying to digitize some small figures appearing in the supporting information file) because the paper states than: “Upward slopes (n = 363) outnumbered downward slopes (n = 139), and 88% of significant trends (p < 0.05) were positive” without saying how many significant trends had been found. If of the remaining 34 studies, we consider only studies where OC has been measured as such (even leaving studies where correlations with surrogate parameters have been used for some periods of missing data), 27 studies remain. Finally, if we do not consider the studies where absolutely no information is given about the analytical methods used, we are left with no more than 11. This last filter really reveals the limitations imposed by the lack of adequately reporting.

In conclusion, it is clear that OC concentrations have increased in some surface waters in the Northern Hemisphere since the 1990s. However, it cannot be proved that it is a general phenomenon because of the lack of data –temporal series– in many parts of the world and, as this study discusses, in the areas for which such series exist, the reporting and methodological problems in the published studies prevent so far reaching a conclusion about the existence of a general temporal behavior of OC concentrations.

5. Recommendations for Future Work

Apart from the many different “technical” questions discussed along the different parts of this critical review, and that can be easily gleaned from the text, the main lesson to be learned from the situation described is the urgent need to improve the way both analytical and data treatment methods are reported.

Obviously, since this type of studies use already existing data, there is no room for improvement of their quality. The quality and types of data varies widely making it hard to analyze and harder still to use to detect temporal trends. However, it is possible, and extremely necessary, that raw data quality is carefully evaluated and that all details are given concerning analytical procedures used, data censoring, etc. Current publishing possibilities also make it feasible to make raw data easily available to all readers (e.g., through Supporting Information files, web-accessible files, etc.) facilitating appraisal and reuse.

Concerning data treatment, apart from the need to apply non-parametric methods –which are already widely used by this research community– there is no “best method” to recommend. However, methods used and any data pre-treatment applied should be, again, carefully detailed. Now that organic carbon concentrations have been determined for some systems for more than 30 years, systematic testing for cyclicity should be strongly encouraged in order to detect any possible climate-driven trend.

Finally, better citing practices will be welcome. Careful reading of the introduction of the 63 papers considered show a worrying repetition of the “accepted belief” that organic carbon concentration increases are widespread and a few papers are repeatedly cited. Citation “is not simply an impartial scholarly method for joining related published knowledge” [7]. Authors should probably be less guided by inertia when making the choice of which results to cite and which to ignore.


We thank the librarians of the old Center for Environmental Sciences (CCMA-CSIC) and of the National Museum of Natural History (MNCN-CSIC) for their help.

Authors Contributions

Montserrat Filella conceived the subject of the article and wrote it. Juan Carlos Rodríguez-Murillo contributed to gathering information and writing the article.





chemical oxygen demand


dissolved organic carbon


dissolved organic matter


gas chromatography


high temperature combustion




locally weighted scatterplot smooth


lineal regression


Mann-Kendall test


organic carbon


particulate organic carbon


particulate organic matter


seasonal Mann-Kendall test


total organic carbon


total organic nitrogen




wet carbon oxidation

Conflicts of Interest

The authors declare no conflict of interest.


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