JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, G03001, doi:10.1029/2011JG001928, 2012
Dissolved organic carbon and chromophoric dissolved organic
matter properties of rivers in the USA
Robert G. M. Spencer,1 Kenna D. Butler,2 and George R. Aiken2
Received 15 December 2011; revised 16 May 2012; accepted 20 May 2012; published 3 July 2012.
[1] Dissolved organic carbon (DOC) concentration and chromophoric dissolved organic
matter (CDOM) parameters were measured over a range of discharge in 30 U.S. rivers,
covering a diverse assortment of fluvial ecosystems in terms of watershed size and
landscape drained. Relationships between CDOM absorption at a range of wavelengths
(a254, a350, a440) and DOC in the 30 watersheds were found to correlate strongly and
positively for the majority of U.S. rivers. However, four rivers (Colorado, Colombia,
Rio Grande and St. Lawrence) exhibited statistically weak relationships between CDOM
absorption and DOC. These four rivers are atypical, as they either drain from the Great
Lakes or experience significant impoundment of water within their watersheds, and
they exhibited values for dissolved organic matter (DOM) parameters indicative of
autochthonous or anthropogenic sources or photochemically degraded allochthonous
DOM and thus a decoupling between CDOM and DOC. CDOM quality parameters in
the 30 rivers were found to be strongly correlated to DOM compositional metrics
derived via XAD fractionation, highlighting the potential for examining DOM
biochemical quality from CDOM measurements. This study establishes the ability to
derive DOC concentration from CDOM absorption for the majority of U.S. rivers,
describes characteristics of riverine systems where such an approach is not valid, and
emphasizes the possibility of examining DOM composition and thus biogeochemical
function via CDOM parameters. Therefore, the usefulness of CDOM measurements,
both laboratory-based analyses and in situ instrumentation, for improving spatial and
temporal resolution of DOC fluxes and DOM dynamics in future studies is considerable
in a range of biogeochemical studies.
Citation: Spencer, R. G. M., K. D. Butler, and G. R. Aiken (2012), Dissolved organic carbon and chromophoric dissolved
organic matter properties of rivers in the USA, J. Geophys. Res., 117, G03001, doi:10.1029/2011JG001928.
1. Introduction
[2] Dissolved organic matter (DOM) plays a multifaceted
role in aquatic ecosystems and represents a fundamental player
in global carbon budgets. DOM takes part in a range of processes within freshwater environments including biological,
chemical and physical transformations [Jaffé et al., 2008]. The
flux of DOM derived from terrestrial net ecosystem production on entering aquatic environments represents an essential
link between terrestrial and aquatic ecosystems and dissolved
organic carbon (DOC) is the most important intermediate in
the global carbon cycle fueling microbial metabolism [Cole
et al., 2007; Battin et al., 2008]. For instance, riverine export
of DOC provides the largest flux of reduced carbon from land
1
Global Rivers Group, Woods Hole Research Center, Falmouth,
Massachusetts, USA.
2
United States Geological Survey, Boulder, Colorado, USA.
Corresponding author: R. G. M. Spencer, Global Rivers Group, Woods
Hole Research Center, 149 Woods Hole Rd., Falmouth, MA 02540, USA.
([email protected])
©2012. American Geophysical Union. All Rights Reserved.
0148-0227/12/2011JG001928
to ocean (0.25 Pg C yr1), as current POC flux estimates are
lower (0.18 Pg C yr1), and underpins biogeochemical cycling
in coastal margins [Hedges et al., 1997; Battin et al., 2008].
With respect to human health, DOM is a water quality constituent of concern as it has been shown to play a role in the
formation of carcinogenic and mutagenic disinfection byproducts [Weishaar et al., 2003; Chow et al., 2007] and has also
been linked to the transport and reactivity of toxic substances
such as mercury [Dittman et al., 2009; Aiken et al., 2011;
Bergamaschi et al., 2011]. Therefore, understanding the production, transport and fate of DOM in aquatic ecosystems is of
direct relevance to studies addressing issues from water quality
to bacterioplankton community structure and function [Crump
et al., 2009; Krupa et al., 2012]. Consequently, DOC concentration and DOM composition data for rivers and streams
are of interest to a diverse range of scientists and engineers
across an assortment of environmental disciplines.
[3] The concentration of DOC in streams and rivers typically ranges from approximately 0.5–50 mgL1 and is linked
to climate and watershed characteristics [Mulholland, 2003].
Although DOC concentration is an extremely important
measurement for deriving fluxes across the landscape and
G03001
1 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
Figure 1. Map of the study sites within the U.S. See Table 1 for site names and details.
examining temporal and spatial trends, it provides little
information about the biochemical composition or quality
of DOM and hence its biogeochemical role [Hood et al.,
2006; Jaffé et al., 2008; Fellman et al., 2009]. Colored or
chromophoric dissolved organic matter (CDOM) parameters
have been linked to DOM molecular weight [Peuravuori
and Pihlaja, 1997; Helms et al., 2008] and composition in
a number of recent studies [Boyle et al., 2009; Spencer et al.,
2010a; Osburn and Stedmon, 2011]. Furthermore, the ability
to not only examine DOM quality but also its biogeochemical processing (e.g., photochemical or microbial degradation)
has previously been related to CDOM parameters [Cory et al.,
2007; Fellman et al., 2009; Mann et al., 2012]. These relatively straightforward and inexpensive CDOM measurements
can be undertaken with small volumes of water, and recent
developments now allow for the possibility of in situ observations [Spencer et al., 2007; Saraceno et al., 2009; Pellerin
et al., 2012]. The prospect of high-resolution in situ CDOM
measurements is opening up the potential for analyses at the
temporal and spatial scales required to truly understand DOM
dynamics and variability in freshwater ecosystems.
[4] Recent studies have examined the utility of CDOM
measurements to derive DOC concentration and examine
DOM composition in specific catchment types (e.g.,
northern high-latitude rivers) [Spencer et al., 2009a].
However, their applicability across a gradient of watershed
types including watershed size and landscape drained has to
date not yet been addressed. This study examined 30 fluvial
sites in the U.S. draining all dominant land cover classes
within the U.S. and ranging in size from small headwater
streams to the mouth of the Mississippi. The aims of this
study were twofold: first, we investigated the possibility of
relating CDOM to DOC concentration in the comprehensive
range of sites studied. We also tried to determine whether
there are any unique features of the watersheds where the
CDOM-DOC relationship breaks down that could explain
these systems’ unsuitability for such an approach. Second,
we tested the possibility of utilizing CDOM parameters to
address DOM composition across the range of watersheds
examined and determined which CDOM parameters may be
of the greatest utility to future investigators seeking to
address DOM quality in fluvial systems.
2. Materials and Methods
2.1. Study Sites
[5] Thirty sites were examined in this study with the aim of
covering the diverse range of watersheds found within the
United States, with respect to both watershed size and
2 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
Table 1. Riverine Study Site Numbers as Shown in Figure 1
River Number
River Name
River
Abbreviation
n
Sampling
Period
Watershed Size
(km2)
Latitude
Longitude
Daily
MaxQ/MinQ
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Androscoggin
Atchafalaya
Colorado
Columbia
Edisto
Evergreen
Fishing Brook
Hubbard Brook
Hudson
Little Wekiva
Lookout Creek
Lower Atchafalaya
Mississippi
Mobile
Neversink
Oak Creek
Passadumkeag
Penobscot
Pike
Porcupine
Potomac
Rio Grande
Sacramento
San Joaquin
St. Lawrence
St. Marys
Susquehanna
Tanana
Yukon at Eagle Village
Yukon at Pilot Station
AND
ATC
COL
CUA
EDI
EVR
FBR
HBR
HUD
LWK
LCR
LAT
MIS
MOB
NEV
OCR
PAS
PEN
PIK
POR
POT
RIG
SAC
SAJ
STL
STM
SUS
TAN
YRE
YRP
12
27
27
18
15
15
21
31
21
14
12
32
29
25
14
12
27
62
14
35
21
27
22
23
28
14
21
30
28
57
2006–2007
2008–2010
2008–2010
2009–2010
2005–2008
2002–2005
2007–2009
2005–2007
2005–2009
2002–2006
2002–2004
2008–2010
2008–2010
2008–2010
2005–2006
2002–2004
2004–2007
2004–2008
2002–2004
2001–2010
2008–2010
2008–2010
2008–2010
2008–2010
2008–2010
2002–2006
2008–2010
2000–2007
2000–2002
2001–2010
8894
241687
638950
665367
7071
167
65
0.13
498
115
62
246308
2926686
111369
172
65
769
19460
660
76405
29966
456700
59569
35058
773888
1810
70188
66304
293964
831386
43.92
30.69
32.72
46.18
33.03
45.07
43.98
44.57
43.97
28.70
44.21
29.69
29.86
31.09
41.89
42.93
45.18
44.83
45.50
66.99
38.93
25.88
38.46
37.68
45.01
30.36
39.66
64.57
64.79
61.93
69.97
91.74
114.72
123.18
80.39
88.68
74.27
72.25
74.13
81.39
122.26
91.22
89.98
87.98
74.59
87.87
68.47
68.70
88.00
143.14
77.12
97.45
121.50
121.27
74.80
82.08
76.17
149.09
141.20
162.88
35.4
8.3
12.7
7.0
11.2
24.1
—–
4699.2
—–
—–
125.0
8.3
6.1
7.5
193.2
420.8
—–
31.2
12.5
276.5
165.6
191
12.9
58.1
1.6
1970.4
304.2
16.1
21.3
31.1
landscape drained (Figure 1, Table 1). For example, watersheds range in size from headwater streams (e.g., Hubbard
Brook WS6; 0.132 km2) to the mouth of the Mississippi
River (2,926,686 km2). Focus was especially placed on larger
watersheds (e.g., Atchafalaya, Colorado, Columbia, Mississippi, Mobile, Potomac, Rio Grande, Sacramento, San
Joaquin, St. Lawrence, Susquehanna and Yukon) near their
terminus to examine the applicability of utilizing CDOM to
derive DOC export to coastal waters. The rivers chosen also
include a diverse range of terrestrial ecosystems including
permafrost underlain (e.g., Porcupine), forest (e.g., Androscoggin, Evergreen, Penobscot, Pike), agriculturally impacted
(e.g., Mississippi, Sacramento, San Joaquin), urban (e.g.,
Little Wekiva, Oak Creek), swamp (e.g., Edisto, St. Marys),
arid and semi-arid highly regulated systems (e.g., Colorado,
Columbia, Rio Grande) and rivers draining from the Great
Lakes (e.g., St. Lawrence).
2.2. Water Sample Collection and Processing
[6] Water samples were collected across the annual
hydrograph to encompass the range of discharge conditions
for each study site. The majority of samples were collected
as part of the U.S. Geological Survey National Stream Quality
Accounting Network (NASQAN) and National Water Quality
Assessment (NAQWA) programs from 2000–2010 (Table 1).
Sample collection took place over a minimum of two years
and up to a maximum of ten years and all analyses were conducted in one laboratory. All water samples were filtered in the
field through Gelman AquaPrep 600 capsule filters (0.45 mm)
that were pre-rinsed with sample water. The hydrophobic
organic acid fraction (HPOA) was obtained following established protocols [Aiken et al., 1992; Spencer et al., 2010b]. In
brief, samples were acidified to pH 2 using HCl and passed
through a column of XAD-8 resin. The HPOA fraction was
retained on the XAD-8 resin and then back eluted with 0.1 M
NaOH.
2.3. Dissolved Organic Carbon and Chromophoric
Dissolved Organic Matter Analyses
[7] Dissolved organic carbon measurements were carried
out on a heated persulfate oxidation OI Analytical Model
700 TOC analyzer [Aiken, 1992]. UV-visible absorbance
measurements were undertaken within 48 h of collection on
a Hewlett-Packard photo-diode array spectrophotometer
(model 8453) between 200 and 800 nm using a 10 mm
quartz cell. All samples were analyzed at constant laboratory
temperature and sample spectra were referenced to a blank
spectrum of distilled water. All absorbance data presented in
this manuscript are expressed as absorption coefficients, a
(l), in units of m1 [Hu et al., 2002]. Chromophoric DOM
(CDOM) absorption coefficients (Napierian) were calculated
from:
aðlÞ ¼ 2:303AðlÞ=l;
ð1Þ
where A(l) is the measured absorbance and l is the cell path
length in meters. The CDOM absorption ratio at 250 nm to
365 nm was calculated (a250:a365) and SUVA254 values were
derived by dividing the UV absorbance (A) at l = 254 nm
3 of 14
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
G03001
Table 2. Mean (SD) Riverine Dissolved Organic Carbon (DOC), Fraction HPOA and Chromophoric Dissolved Organic Matter
(CDOM) Parameters
River
Abbreviation
DOC
(mgL1)
Fraction HPOA
SUVA254
(LmgC1m1)
a250:a365
S275–295
(103nm1)
AND
ATC
COL
CUA
EDI
EVR
FBR
HBR
HUD
LWK
LCR
LAT
MIS
MOB
NEV
OCR
PAS
PEN
PIK
POR
POT
RIG
SAC
SAJ
STL
STM
SUS
TAN
YRE
YRP
6.4 (0.9)
5.2 (0.9)
3.1 (0.4)
2.0 (0.3)
8.9 (3.4)
4.5 (3.6)
7.3 (1.9)
3.1 (1.1)
5.3 (1.0)
5.3 (2.5)
1.0 (0.4)
5.1 (0.7)
4.1 (0.5)
5.5 (1.2)
2.1 (1.5)
6.7 (1.9)
12.0 (4.3)
9.8 (2.9)
8.0 (5.0)
10.0 (5.9)
3.3 (0.6)
4.8 (0.5)
2.4 (0.8)
3.8 (1.1)
2.7 (0.2)
42.1 (16.0)
2.5 (0.5)
3.2 (1.9)
5.2 (3.0)
8.3 (5.0)
0.56 (0.01)
0.47 (0.04)
0.39 (0.03)
0.42 (0.04)
0.54 (0.04)
0.45 (0.07)
0.54 (0.03)
0.49 (0.04)
0.52 (0.03)
0.46 (0.04)
0.43 (0.09)
0.47 (0.03)
0.45 (0.02)
0.50 (0.03)
0.47 (0.05)
0.44 (0.04)
0.61 (0.04)
0.58 (0.03)
0.52 (0.07)
0.51 (0.05)
0.40 (0.04)
0.35 (0.03)
0.39 (0.05)
0.41 (0.03)
0.28 (0.03)
0.67 (0.06)
0.40 (0.04)
0.46 (0.07)
0.50 (0.06)
0.51 (0.05)
3.59 (0.15)
3.22 (0.33)
1.67 (0.22)
2.62 (0.43)
3.75 (0.29)
3.08 (0.60)
3.89 (0.29)
2.80 (0.27)
3.48 (0.22)
2.85 (0.44)
2.45 (0.35)
3.13 (0.24)
2.99 (0.23)
3.45 (0.34)
2.47 (0.66)
2.86 (0.52)
4.19 (0.30)
3.80 (0.26)
3.71 (0.55)
3.02 (0.55)
2.31 (0.33)
2.03 (0.24)
2.41 (0.51)
2.47 (0.25)
1.31 (0.16)
4.56 (0.28)
2.25 (0.28)
2.68 (0.54)
3.00 (0.64)
3.08 (0.47)
5.05 (0.35)
5.25 (0.45)
9.05 (1.47)
5.89 (1.09)
4.70 (0.23)
4.89 (0.44)
4.81 (0.13)
6.55 (0.86)
5.16 (0.31)
5.50 (0.46)
6.46 (2.32)
5.41 (0.53)
5.45 (0.61)
4.80 (0.53)
6.65 (2.43)
6.19 (0.76)
4.56 (0.17)
4.98 (0.30)
4.87 (0.42)
5.72 (1.14)
5.73 (0.77)
7.38 (0.84)
5.34 (0.72)
5.93 (1.21)
9.65 (2.30)
4.20 (0.27)
5.79 (0.68)
5.66 (1.10)
5.93 (1.12)
5.52 (0.90)
14.53 (0.76)
14.85 (0.80)
21.69 (1.56)
16.33 (1.37)
13.32 (0.41)
13.62 (0.40)
13.85 (0.62)
16.02 (1.14)
14.64 (0.73)
16.02 (1.05)
13.19 (1.10)
15.32 (0.83)
15.14 (0.80)
14.27 (1.63)
15.53 (1.65)
15.46 (1.20)
13.38 (0.59)
14.13 (0.89)
14.28 (1.61)
15.54 (2.06)
15.74 (1.71)
19.80 (1.45)
15.69 (1.43)
15.71 (0.73)
22.96 (1.76)
12.47 (0.78)
15.33 (1.57)
16.13 (1.78)
15.95 (2.02)
15.26 (1.87)
by the DOC concentration (mgL1) and is reported in the
units of liter per milligram carbon per meter [Weishaar et al.,
2003]. The spectral slope parameter (S) was calculated using
a nonlinear fit of an exponential function to the absorption
spectrum in the ranges of 275–295 nm and 350–400 nm
using the equation:
S350–400
(103nm1)
16.69
16.63
18.99
16.87
16.92
15.97
17.31
20.03
17.32
16.98
16.18
16.88
16.56
16.23
17.86
17.92
16.44
17.05
17.04
18.17
16.58
17.76
16.35
16.87
18.95
16.65
17.34
17.77
18.17
17.89
(0.62)
(0.88)
(2.09)
(1.13)
(0.64)
(0.89)
(0.28)
(1.56)
(0.27)
(0.59)
(2.56)
(0.86)
(1.24)
(0.75)
(1.27)
(1.05)
(0.24)
(0.33)
(0.29)
(1.67)
(1.79)
(1.36)
(1.26)
(0.87)
(3.17)
(0.57)
(1.45)
(1.90)
(2.14)
(1.45)
SR
0.87 (0.04)
0.89 (0.04)
1.15 (0.09)
0.97 (0.06)
0.79 (0.03)
0.86 (0.05)
0.80 (0.03)
0.80 (0.05)
0.84 (0.04)
0.94 (0.05)
0.82 (0.09)
0.91 (0.04)
0.92 (0.06)
0.88 (0.08)
0.86 (0.10)
0.86 (0.06)
0.81 (0.04)
0.83 (0.04)
0.80 (0.07)
0.86 (0.08)
0.96 (0.13)
1.12 (0.11)
0.96 (0.07)
0.93 (0.04)
1.23 (0.16)
0.75 (0.06)
0.89 (0.14)
0.89 (0.06)
0.88 (0.07)
0.85 (0.07)
3. Results
1992]. A higher fraction HPOA therefore typically indicates
an increased contribution from allochthonous organic matter
sources (i.e., terrestrial), whereas a lower fraction HPOA is
indicative of organic matter from autochthonous sources (i.e.,
algal or microbial) or photodegraded DOM [McKnight and
Aiken, 1998; Cory et al., 2007]. For example, microbially
dominated Antarctic lakes have been shown to have a fraction HPOA of approximately 0.23 [Aiken et al., 1992],
whereas allochthonous-dominated aquatic systems have
greater fraction HPOA values (e.g., Arctic blackwater stream =
0.47 [Cory et al., 2007] and Suwannee River = 0.58 [Aiken
et al., 1992]). Increasing fraction HPOA is also important
with respect to toxic substances such as mercury as it acts as
a ligand and studies have shown strong positive linear relationships between the fraction HPOA and dissolved mercury
concentration [Schuster et al., 2008; Dittman et al., 2009].
3.1. Bulk Dissolved Organic Carbon and Fractionation
[8] Mean riverine DOC concentrations ranged from 1.0
mgL1 (0.4 SD) in Lookout Creek to 42.1 mgL1 (16.0 SD) in St. Marys (Table 2, Figures 2a–2b). The majority of
U.S. rivers had mean riverine DOC concentrations between
2.0–10.0 mgL1. Mean fraction HPOA ranged from 0.28
(0.03 SD) in the St. Lawrence to 0.67 (0.06 SD) in
St. Marys and the bulk of rivers studied had a mean fraction
HPOA between 0.40–0.60 (Table 2, Figures 2c–2d). The
HPOA fraction has historically been described as primarily
composed of fulvic acid with the remainder as humic acid
and thus represents the high molecular weight, aromatic
carbon-dominated fraction of DOM [Aiken et al., 1979,
3.2. Chromophoric Dissolved Organic Matter
[9] Mean SUVA254 values in the rivers examined ranged
from 1.31 L mg C1 m1 (0.16 SD) for the St. Lawrence to
4.56 L mg C1 m1 (0.28 SD) in St. Marys (Table 2,
Figures 3a–3b). The majority of the rivers examined in
this study had mean SUVA254 values between 2.00 and
3.80 L mg C1 m1 (Table 2, Figures 3a–3b). SUVA254
values have been positively correlated to the percent aromaticity of DOM as measured by 13C-NMR [Weishaar et al.,
2003]. The lowest mean SUVA254 values observed in U.S.
rivers are comparable to values reported for HPOA isolates
from microbial-dominated end-members such as Pony Lake
(1.7 L mg C1 m1) and Lake Fryxell (1.8 L mg C1 m1;
aðlÞ ¼ aðlref Þesðllref Þ ;
ð2Þ
where a(l) is the absorption coefficient of CDOM at a
specified wavelength, lref is a reference wavelength and S is
the slope fitting parameter. The spectral slope ratio (SR) was
calculated as the ratio of S275–295 to S350–400 [Helms et al.,
2008].
4 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
Figure 2. Box plots of (a–b) DOC, and (c–d) fraction of hydrophobic organic acid fraction (HPOA) for
the 30 rivers. Note the different y axis scale between Figures 2a and 2b. The black dotted line and the black
solid line represent the mean and the median, respectively. The horizontal edges of the boxes denote the
25th and 75th percentiles, the whiskers denote the 10th and 90th percentiles, and black circles represent
outliers.
Weishaar et al., 2003], and aquatic systems with little vascular
plant input (e.g., groundwaters: 1.3–1.6 L mg C1 m1)
[Spencer et al., 2008]. Similarly, the highest mean SUVA254
values reported in this study are comparable to values
for HPOA isolates from allochthonous-dominated endmembers (e.g., Ogeechee and Suwannee Rivers; 3.2–
5.3 L mg C1 m1) [Weishaar et al., 2003] and aquatic systems with significant vascular plant inputs (e.g., blackwaters:
3.4–4.5 L mg C1 m1) [Spencer et al., 2008, 2010a].
[10] The a250:a365 ratio has previously been related to the
aromatic content and molecular size of DOM with increasing
values indicating a decrease in aromaticity and molecular
size [Peuravuori and Pihlaja, 1997]. Mean a250:a365 values
ranged from 4.20 (0.27 SD) in St. Marys to 9.65 (2.30 SD) in the St. Lawrence, with the bulk of a250:a365 mean
values in the rivers examined ranging from 5.00–6.50
(Table 2, Figures 3c–3d). The lowest and highest mean a250:
a365 values in St. Marys and the St. Lawrence are comparable
to allochthonous-dominated blackwaters of the Great Dismal
Swamp (4.57–4.64) and coastal waters (e.g., Georgia Bight =
8.7 1.4), respectively [Helms et al., 2008].
[11] The spectral slope parameter (S) describes the spectral
dependence of the CDOM absorption coefficient and as a
result provides information with respect to the quality of the
CDOM [Blough and Del Vecchio, 2002). S has been shown
to vary with the source of CDOM and also to be sensitive to
biological and photochemical alteration of the source material [Stedmon and Markager, 2001; Obernosterer and
Benner, 2004; Osburn and Stedmon, 2011]. Typically, a
steeper S value has been related to a decrease in molecular
weight and aromaticity of DOM [Blough and Green, 1995;
Helms et al., 2008]. Historically, S has been calculated over
a range of wavelengths and 275–295 nm (S275–295) and 350–
400 nm (S350–400) were chosen because Helms et al. [2008]
in their extensive study of S in a range of aquatic ecosystems and DOM sources observed the first derivative of the
natural log spectra had the greatest variation in these ranges.
The slope ratio (SR) of S275–295: S350–400 has also been
5 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
Figure 3. Box plots of (a–b) SUVA254, and (c–d) a250:a365 for the 30 rivers. The black dotted line and
the black solid line represent the mean and the median, respectively. The horizontal edges of the boxes
denote the 25th and 75th percentiles, the whiskers denote the 10th and 90th percentiles, and black circles
represent outliers.
shown to be sensitive to characterizing CDOM in natural
waters, with lower relative values indicative of DOM of
higher molecular weight, greater aromaticity and increasing
vascular plant inputs [Helms et al., 2008; Spencer et al.,
2010a; Osburn et al., 2011].
[12] Mean S275–295 values ranged from 12.47 103 nm1
(0.78 SD) in St. Marys to 22.96 103 nm1 (1.76 SD)
in the St. Lawrence and the majority of U.S. rivers exhibited
S275–295 values between 13.00–16.50 103 nm1 (Table 2,
Figures 4a–4b). The shallowest mean S275–295 values are
comparable to allochthonous-dominated waters such as the
Congo River (12.34 103 nm1) [Spencer et al., 2009b], the
Yukon River at the peak of the freshet (12.28 103 nm1)
[Spencer et al., 2009a] and the Great Dismal Swamp (12.7–
12.8 103 nm1) [Helms et al., 2008]. The steepest mean
S275–295 values are comparable to data from U.S. coastal
waters (e.g., 24.00 103 nm1 in the Georgia Bight [Helms
et al., 2008] and 22.00–28.00 103 nm1 in surface waters
of the northern Gulf of Mexico [Shank and Evans, 2011]), and
the minimum values for lakes in the Great Plains (e.g., 22.18 103 nm1) [Osburn et al., 2011], which represent DOM
from autochthonous sources and photochemically degraded
allochthonous DOM. Mean S350–400 values followed a similar
trend to S275–295 values but covered a narrower range with
shallowest values in Evergreen River of 15.97 103 nm1
(0.89 SD) and steepest values in Hubbard Brook of 20.03 103 nm1 (1.56 SD) (Table 2, Figures 4c–4d). Most
U.S. rivers had S350–400 values between 16.50–18.25 103 nm1. As observed for S275–295, the steepest mean
S350–400 values are comparable to previously reported data
for coastal waters (18.00–19.00 103 nm1) [Shank and
Evans, 2011] and prairie lakes (22.41 103 nm1) [Osburn
et al., 2011]. Similarly, the shallowest mean S350–400 values
are analogous to data reported from aquatic ecosystems with
high allochthonous inputs such as the Congo River (15.21 103 nm1) [Spencer et al., 2009b].
[13] The mean SR values ranged from 0.75 (0.06 SD) in
St. Marys to 1.23 (0.16 SD) in the St. Lawrence, with the
6 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
Figure 4. Box plots of (a–b) S275–295, (c–d) S350–400, and (e–f) SR for the 30 rivers. Note the different y axis
scale between Figures 4a and 4b and between Figures 4e and 4f. The black dotted line and the black solid
line represent the mean and the median, respectively. The horizontal edges of the boxes denote the 25th and
75th percentiles, the whiskers denote the 10th and 90th percentiles, and black circles represent outliers.
majority of U.S. rivers ranging from 0.80–0.95 (Table 2,
Figures 4e–4f). Lower mean SR values are similar to data
reported from Arctic rivers at the peak of the freshet (e.g.,
Yukon = 0.79; Yenisey = 0.79) when they receive significant vascular plant inputs [Spencer et al., 2009a; Stedmon et
al., 2011], or blackwater tropical rivers during the onset of
the wet season (0.79) [Spencer et al., 2010a]. The highest
mean SR values are comparable to mean values from prairie
lakes (1.36) [Osburn et al., 2011] and coastal waters (1.20–
1.40) [Shank and Evans, 2011].
4. Discussion
4.1. Deriving DOC Concentration From CDOM
in U.S. Rivers
[14] Historically, relationships between CDOM and DOC
have principally been examined in coastal waters [Ferrari
7 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
Figure 5. Relationships between dissolved organic carbon
(DOC) and chromophoric dissolved organic matter (CDOM)
absorption (a254, black circles, black line; a350, gray circles,
gray line; and a440, white circles, black dashed line): (a) Mississippi River and (b) Hubbard Brook.
et al., 1996; Vodacek et al., 1997; Rochelle-Newall and
Fisher, 2002]. Although CDOM represents only a fraction
of the total DOC pool a number of studies have reported
strong correlations between CDOM properties and DOC
concentration in coastal waters [see Del Vecchio and Blough,
2004, and reference therein; Mannino et al., 2008]. The
investigation of CDOM and DOC relationships in riverine
environments is also extremely pertinent to facilitate the
development of improved DOC flux estimates through
increased temporal coverage via recently developed in situ
instrumentation [Spencer et al., 2007; Saraceno et al., 2009;
G03001
Downing et al., 2009; Pellerin et al., 2012]. Furthermore, a
recent study by Griffin et al. [2011] highlighted the potential
to derive DOC via CDOM in a major river (Kolyma, Siberia)
using Landsat satellite imagery. Therefore, if robust CDOM
versus DOC relationships can be derived for rivers they can
be utilized to examine shifts in the export and timing of the
flux of DOC in highly dynamic periods (e.g., storms, snowmelt) in small watersheds when the majority of DOC export
occurs [Inamdar et al., 2006; Saraceno et al., 2009; Pellerin
et al., 2012], and also to improve estimates of the land-ocean
flux of terrestrial DOC from major rivers [Spencer et al.,
2009a]. To assess this objective in the diverse range of U.S.
rivers studied here with respect to both watershed size and
landscape drained we examined CDOM absorption relationships at a254, a350 and a440 to DOC (Figure 5, Table 3).
[15] Absorption coefficients at 254, 350 and 440 nm correlated strongly and positively with DOC concentration for the
majority of U.S. rivers (Table 3). Examples of relationships
between DOC and a254, a350 and a440 are shown for the
smallest (Hubbard Brook WS6) and largest (Mississippi)
watersheds studied in Figure 5. The relationship between
absorption coefficient and DOC concentration varied between
the wavelengths studied with typically stronger relationships
observed at shorter wavelengths. CDOM absorption spectra
typically decrease in an approximately exponential fashion
with increasing wavelength, and so the accuracy of CDOM
measurements decreases at longer wavelengths resulting in a
weakening in the correlation [Baker et al., 2008]. This is particularly the case for samples exhibiting low CDOM absorption values.
[16] In the U.S. rivers examined in this study a number of
rivers consistently standout as having statistically weak relationships between CDOM and DOC concentration. The Rio
Grande (r2 = 0.453; p = 0.0001) and the St. Lawrence (r2 =
0.206; p = 0.0154) are the only two rivers that do not exhibit
a statistically significant relationship at the <0.0001 significance level for DOC versus a254, with the relationship
explaining over 70% of the variance in all other rivers
(Table 3). With respect to DOC versus a350 the Rio Grande
and the St. Lawrence exhibit weak correlations (r2 = 0.265; p =
0.0060 and r2 = 0.037; p = 0.3279, respectively), as does the
Colombia (r2 = 0.431; p = 0.0031), with the relationship
explaining over 55% of the variance in all other rivers at a
significance level of <0.0005 (Table 3). Similarly, for DOC
versus a440 the Colombia (r2 = 0.138; p = 0.130), the Rio
Grande (r2 = 0.0140; p = 0.557) and the St. Lawrence (r2 =
0.0034; p = 0.769) stand out as having weak correlations, and
the Colorado also shows a poor correlation (r2 = 0.181; p =
0.0268) with respect to DOC versus a440. These four rivers
(Colorado, Colombia, Rio Grande and St. Lawrence) represent
in many ways atypical systems from the other rivers in
this study, as they all exhibit values for DOM parameters
indicative of autochthonous or anthropogenic sources, or
photochemically degraded allochthonous DOM (Table 2,
Figures 2–4). The St. Lawrence (the river with the weakest
correlations between DOC and absorption coefficients) exhibits the lowest mean values for fraction HPOA and SUVA254
and the highest a250:a365, S275–295 and SR values of all rivers
studied (Table 2, Figures 2–4). Draining from the Great Lakes
the St. Lawrence is dominated by autochthonous DOM
[Cotner et al., 2004; Sterner, 2010] and photochemically
modified allochthonous DOM [Biddanda and Cotner, 2003;
8 of 14
r2
0.930
0.904
0.903
0.701
0.969
0.994
0.935
0.937
0.887
0.983
0.913
0.869
0.843
0.823
0.982
0.957
0.966
0.951
0.999
0.988
0.855
0.453
0.945
0.961
0.206
0.975
0.822
0.966
0.982
0.985
River
AND
ATC
COL
CUA
EDI
EVR
FBR
HBR
HUD
LWK
LCR
LAT
MIS
MOB
NEV
OCR
PAS
PEN
PIK
POR
POT
RIG
SAC
SAJ
STL
STM
SUS
TAN
YRE
YRP
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0001
<0.0001
<0.0001
0.0154
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
P
9.08
10.8
7.44
9.87
8.86
10.9
10.6
6.99
7.67
8.77
6.37
9.88
8.91
8.47
9.30
9.38
9.68
8.52
10.6
8.16
8.19
5.66
10.4
7.35
2.20
9.75
6.67
8.01
9.18
8.33
Slope
0.829
0.706
0.489
1.61
0.438
0.226
0.553
0.337
0.627
0.335
0.623
0.699
0.740
0.819
0.368
0.629
0.362
0.249
0.083
0.156
0.775
1.24
0.563
0.325
0.848
0.456
0.712
0.286
0.243
0.138
5.38
3.71
1.54
3.26
4.17
1.29
4.16
1.11
3.37
1.95
0.686
3.613
3.06
4.59
0.934
4.36
4.62
2.55
0.766
1.81
2.58
5.96
1.45
1.28
2.33
21.9
1.80
1.07
1.46
1.33
5.23
17.1
11.1
7.57
1.33
12.9
11.3
1.58
1.67
9.63
0.832
13.6
8.25
2.98
5.51
17.5
0.287
2.16
11.6
7.01
9.19
4.65
10.9
5.84
2.21
20.4
3.67
4.40
8.66
6.74
0.769
0.837
0.740
0.431
0.940
0.992
0.930
0.915
0.791
0.973
0.857
0.785
0.707
0.557
0.969
0.848
0.935
0.934
0.998
0.981
0.714
0.265
0.929
0.889
0.037
0.981
0.642
0.915
0.952
0.938
r2
0.0004
<0.0001
<0.0001
0.0031
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.0060
<0.0001
<0.0001
0.328
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
P
2.56
3.28
1.83
2.42
2.41
3.09
3.02
1.82
2.05
2.35
2.00
3.02
2.82
2.13
2.59
2.38
2.87
2.57
2.97
2.29
2.39
1.28
3.38
1.75
0.275
3.57
1.65
2.03
2.58
2.43
Slope
0.467
0.290
0.217
0.694
0.169
0.0756
0.196
0.103
0.241
0.114
0.258
0.289
0.350
0.397
0.133
0.319
0.127
0.0881
0.0342
0.0560
0.346
0.427
0.209
0.135
0.275
0.144
0.282
0.117
0.114
0.0844
Standard
Error
Standard
Error
Standard
Error
Int.
a350
a254
2.55
7.01
3.76
1.93
0.281
3.88
3.91
1.21
0.117
3.33
0.634
6.27
4.54
0.278
2.02
5.50
0.936
2.13
3.71
3.88
3.52
1.85
4.42
1.44
0.546
13.7
1.03
1.26
3.69
3.70
Int.
3.03
1.52
0.684
1.40
1.61
0.429
1.47
0.338
1.30
0.664
0.284
1.50
1.45
2.22
0.339
2.21
1.62
0.903
0.318
0.648
1.15
2.05
0.536
0.532
0.757
6.46
0.713
0.438
0.683
0.816
Standard
Error
Table 3. Relationships Between Dissolved Organic Carbon (DOC) and CDOM Absorption Coefficients (a254, a350 and a440)
0.499
0.699
0.181
0.138
0.820
0.961
0.927
0.608
0.549
0.949
0.344
0.495
0.364
0.306
0.833
0.588
0.918
0.891
0.915
0.949
0.535
0.0140
0.868
0.628
0.0034
0.960
0.336
0.501
0.779
0.617
r2
0.0152
<0.0001
0.0268
0.130
<0.0001
<0.0001
<0.0001
<0.0001
0.0001
<0.0001
0.0451
<0.0001
0.0005
0.0041
<0.0001
0.0036
<0.0001
<0.0001
<0.0001
<0.0001
0.0002
0.557
<0.0001
<0.0001
0.769
<0.0001
0.0157
<0.0001
<0.0001
<0.0001
P
0.647
0.941
0.404
0.558
0.523
0.672
0.682
0.323
0.482
0.580
0.498
0.949
1.10
0.450
0.575
0.481
0.713
0.641
0.624
0.555
0.669
0.0988
1.07
0.423
0.0672
0.802
0.288
0.375
0.640
0.701
Slope
0.216
0.123
0.172
0.350
0.0679
0.0376
0.0452
0.0482
0.100
0.0387
0.218
0.175
0.280
0.141
0.0742
0.127
0.0428
0.0289
0.0548
0.0219
0.143
0.166
0.0930
0.0710
0.227
0.0474
0.119
0.0708
0.0669
0.0745
Standard
Error
a440
0.806
2.45
1.03
0.476
0.311
0.747
1.00
0.273
0.233
1.02
0.366
2.64
2.76
0.442
0.603
1.21
0.391
0.942
0.438
1.25
1.24
0.357
1.71
0.538
0.340
2.19
0.0940
0.0921
1.25
1.76
Int.
1.40
0.648
0.542
0.707
0.647
0.214
0.340
0.158
0.538
0.226
0.240
0.905
1.16
0.792
0.189
0.883
0.546
0.296
0.509
0.254
0.477
0.794
0.239
0.280
0.623
2.12
0.301
0.265
0.401
0.720
Standard
Error
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
9 of 14
G03001
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
Hiriart-Baer et al., 2008]. DOM produced via autochthonous
processes has previously been shown to be decoupled from
DOC accumulation, and photodegradative processes are
known to remove CDOM preferentially over DOC [Moran et
al., 2000; Opsahl and Zepp, 2001; Rochelle-Newall and
Fisher, 2002]. Thus, the lack of a relationship between DOC
and CDOM absorption coefficients in the St. Lawrence is not
surprising due to the dissociation between the CDOM and
DOC pools.
[17] The Colorado, Colombia and Rio Grande rivers also
exhibit fraction HPOA values and CDOM parameters indicative of systems dominated by autochthonous DOM or DOM
derived from anthropogenic sources (e.g., wastewater), or
photochemically degraded allochthonous DOM (Table 2,
Figures 2–4). The Colorado is heavily regulated with over
20 major dams and extensive reservoirs along its course,
including the two largest reservoirs in the U.S. (Lake Mead
and Lake Powell). The Columbia has also been significantly
dammed with 14 major dams on the main stem including the
Grand Coulee Dam (impounding the sixth largest reservoir in
the U.S.). The Rio Grande is also extensively modified with
significant water withdrawal, water impoundment (e.g.,
Amistad Dam, Falcon Dam, Elephant Butte Dam) and levee
construction disconnecting the river from its floodplains
[Valett et al., 2005]. These three rivers all drain semi-arid to
arid areas in the intermontane plateaus of the U.S. western
states that receive intense solar irradiance. Therefore, with the
significant impoundment of water within these watersheds it
seems apparent that an increased relative contribution of
groundwater and wastewater derived DOM in these systems,
or autochthonous production along with potentially photochemical degradation of allochthonous DOM within reservoirs
leads to the DOM exported exhibiting low values for fraction
HPOA and SUVA254 and high a250:a365, S275–295 and SR
values in comparison to the majority of rivers studied (Table 2,
Figures 2–4), and a decoupling of CDOM and DOC (Table 3).
[18] The strong correlations observed between DOC and
a254, a350 and a440 for U.S. rivers except the Colorado,
Colombia, Rio Grande and St. Lawrence are indicative of
the dominance of predominantly allochthonous DOM in
these watersheds (Table 3, Figure 5). The relationship
between DOC and CDOM absorption for all U.S. rivers also
generally exhibits a negative intercept and is always potentially negative within standard error (Table 3) clearly
showing that not all DOC is chromophoric. The DOC versus
CDOM linear regression lines typically also become steeper
(i.e., increased absorption per unit DOC) in rivers with
greater vascular plant derived character. For instance, St.
Marys has the highest fraction HPOA and SUVA254 values
and lowest a250:a365, S275–295 and SR values, and for DOC
versus a350 the steepest slope (3.57 0.144 SE; Table 3).
Conversely, the Rio Grande that has a much lower fraction
HPOA and SUVA254 values and higher a250:a365, S275–295
and SR values, has a relatively shallower slope for DOC
versus a350 (1.28 0.427 SE; Table 3). The greater vascular
plant derived character of St. Marys reflect its source in one
of the largest freshwater wetlands in the World (Okefenokee
Swamp) in comparison to the heavily regulated Rio Grande
with little wetland area within its semi-arid and arid watershed. Therefore, future studies may be able to link both DOC
G03001
versus absorption coefficient relationships and thus improve
DOC export fluxes, as well as CDOM compositional parameters to watershed characteristics such as wetland coverage
or degree of impoundment.
4.2. Examining DOM Composition From CDOM
in U.S. Rivers
[19] The utility of DOC fractionation via XAD-8 resin has
been extensively shown since the 1970s and the isolates
derived from XAD resins are used by the International
Humic Substances Society (IHSS) to produce their wellstudied and widely used reference materials (e.g., Suwannee
River Fulvic Acid). Such DOM reference materials have
been characterized to a degree not historically possible with
wholewaters and have been comprehensively studied in
controlled experiments linking DOM composition to its
properties [Weishaar et al., 2003; Stubbins et al., 2008;
Boyle et al., 2009]. As such fraction HPOA represents a
useful metric for DOM source and processing and has also
been linked to the transport of toxic substances such as
mercury [Cory et al., 2007; Dittman et al., 2009]. However,
fractionation of DOC is expensive, time consuming, requires
large volumes of water (≥1 L) and is analytically demanding,
and so is not logistically feasible for examining DOM
compositional changes at high temporal resolution. However, DOM compositional changes have been reported in
rivers on hourly timescales [Hood et al., 2006; Fellman et
al., 2009], even within large rivers such as the San Joaquin
[Spencer et al., 2007]. Although a number of studies have
examined relationships between components of the DOM
pool (e.g., lignin phenols) [Hernes and Benner, 2003] and
absorption coefficients, few studies have to date investigated
relationships between DOM biochemical and optical properties [Spencer et al., 2010a; Osburn and Stedmon, 2011]. In
order to examine the potential of developing a CDOM
derived proxy for fraction HPOA and thus understand DOM
biogeochemical function (e.g., microbial and photochemical
reactivity) we explored relationships between the fraction
HPOA and SUVA254, a250:a365, S275–295, S350–400 and SR.
[20] Significant linear correlations were observed between
fraction HPOA and SUVA254, a250:a365, S275–295 and SR (r2 =
0.89, r2 = 0.56, r2 = 0.55, r2 = 0.66 respectively; P < 0.0001;
Figure 6). No significant relationship was found between
fraction HPOA and S350–400 although generally samples with
higher mean fraction HPOA exhibited mean shallower S350–
400 values (e.g., St. Marys; HPOA = 0.67; S350–400 = 16.65 103 nm1), and on the contrary samples with lower mean
fraction HPOA had mean steeper S350–400 values (e.g.,
St. Lawrence; HPOA = 0.28; S350–400 = 18.95 103 nm1;
Table 3). The lack of a relationship between fraction HPOA
and S350–400 could be because the range of S350–400 values is
much smaller than observed for S275–295 (Table 2) and so has
a smaller statistical dispersion versus fraction HPOA (i.e., the
greater range in S275–295 potentially allows for clearer distinction versus fraction HPOA). The S275–295 parameter can
also be measured with greater precision, especially in optically clear waters and so is more robust for examination
across a broad range of aquatic systems [Helms et al., 2008].
[21] SUVA254 exhibits a very robust positive relationship
across the 30 rivers studied with fraction HPOA (Figure 6a).
This easily derived parameter from DOC concentration and
10 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
G03001
Figure 6. Relationships between fraction HPOA and CDOM parameters for the 30 rivers: (a) SUVA254,
(b) a250:a365, (c) S275–295, and (d) SR. Black circle represents the mean value for an individual river and
error bars represent the standard deviation.
UV absorbance at 254 nm therefore provides a good surrogate for fraction HPOA. Although SUVA254 is a straightforward measurement requiring little sample volume and can
have a high throughput, it cannot be measured in situ. Solely
CDOM derived parameters such as a250:a365, S275–295 and
SR do have the possibility however to be utilized by future in
situ studies. These three measurements explain between 55%
and 66% of the variance between fraction HPOA and the
CDOM parameters (Figures 6b–6d) and thus highlight the
ability to examine DOM composition from CDOM parameters in riverine systems. A previous study focusing across
a similarly diverse range of surface waters encompassing
North American streams, lakes and estuarine environments
reported a significant relationship between SUVA254 and
DOM C:N ratio [Jaffé et al., 2008]. That study also reported
a significant relationship between DOM C:N ratio and
fluorescence index (the ratio of emission intensities at 470
and 520 nm at an excitation wavelength of 370 nm [Cory
et al., 2010]), which also has the potential to be measured
via in situ technologies. It seems apparent that CDOM can
be utilized in a broad range of freshwater ecosystems to not
only derive improved fluxes of constituents of interest (e.g.,
DOC, lignin phenols, mercury) but also to examine DOM
composition. Such an approach employing recently developed in situ technologies will allow for enhanced understanding of the biogeochemical role that DOM plays in
freshwater ecosystems at relevant temporal resolution, as
well as opening up the possibility for improving spatial
resolution of how DOM is modified within aquatic environments (i.e., from source to sea).
4.3. Future Implications
[22] The ability to derive DOC concentration via CDOM
absorption in the majority of U.S. rivers examined has
implications for improving flux estimates through increased
temporal resolution via in situ instrumentation. The fate of
terrigenous DOC in the ocean remains a pertinent biogeochemical question, and the prospect of improving load estimates for terrigenous DOC and biomarkers such as lignin via
CDOM has ramifications that may help answer this paradox
11 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
[Hedges et al., 1997; Spencer et al., 2009a]. Naturally, calculation of the residence time of terrigenous DOC in the
ocean is dependent on accurately constraining the size of
fluxes and reservoirs, and therefore any technique that can
improve flux estimates is of assistance in improving residence time calculations. Recent studies utilizing CDOM
derived estimates of terrigenous fluxes have shown increased
flux estimates over historical studies due to the ability to
easily increase temporal resolution [Spencer et al., 2009a;
Osburn and Stedmon, 2011]. If such an approach across a
wide range of watersheds led to an increase from past estimates in terrigenous DOC flux to marine environments, this
would highlight a potentially greater role for removal
mechanisms (e.g., photochemical and microbial degradation)
and further underpin recent work showing terrigenous DOC
to be more reactive than historically thought [Holmes et al.,
2008; Alling et al., 2010; Stubbins et al., 2010].
[23] Aquatic ecosystems are currently facing a range of
stressors from climate change, land-use changes (e.g., deforestation, conversion to agriculture, urbanization), engineering
(e.g., channelization, impoundment) and the impacts of pollution. As shown in this study (Figure 6), the ability to not just
examine DOC concentration but also DOM composition via
CDOM holds great potential. For example, DOC flux may not
be changing from an impacted watershed but the quality of
DOM exported may be fundamentally altered in a watershed
in transition [Bernardes et al., 2004]. The biochemical character of DOM is key with respect to its biogeochemical role in
downstream ecosystems [Crump et al., 2009; Mann et al.,
2012]. Therefore, the ability to improve temporal and spatial
resolution of measurements of DOM quality via CDOM
parameters provides an extremely useful mechanism by which
to scale up DOM compositional measurements in future
studies and to assist in delineating the complex role DOM
plays in aquatic environments. Particularly in highly dynamic
periods within watersheds such as storms and spring freshets
when both DOC concentration and DOM composition change
rapidly with concurrent shifts in biolability [Fellman et al.,
2009; Mann et al., 2012], high resolution in situ CDOM
measurements will be of great value for understanding the role
and fate of DOM. Deployment of in situ CDOM instrumentation in freshwater ecosystems is still in its early stages but a
number of studies have examined DOM variability in watersheds [Spencer et al., 2007; Saraceno et al., 2009; Pellerin
et al., 2012]. The data presented in this manuscript clearly
show the ability to derive DOC loads from CDOM and discharge measurements for the majority of U.S. rivers studied,
and also to examine DOM composition and thus biogeochemical function via CDOM parameters which can be measured in situ (e.g., S275–295). Therefore, the potential of CDOM
measurements, both traditional laboratory-based analyses and
in situ instrumentation to improve spatial and temporal resolution of DOC fluxes and DOM dynamics in future studies is
considerable.
[24] Acknowledgments. The authors gratefully acknowledge the
contributions of many U.S. Geological Survey scientists and field personnel
who collected the samples reported on here. This study was supported by
the U.S. Geological Survey National Stream Quality Accounting Network
(http://water.usgs.gov/nasqan), the U.S. Geological Survey National Water
Quality Assessment Program (http://water.usgs.gov/nawqa/), the U.S. Geological Survey National Research Program (http://water.usgs.gov/nrp),
NASA grant NNX09AU89G and NSF ETBC grant 0851101. The use of
brand names in this manuscript is for identification purposes only and does
G03001
not imply endorsement by the U.S. Geological Survey. For producing
Figure 1, we thank Greg Fiske at the Woods Hole Research Center, and
for their helpful comments on the manuscript, we thank two anonymous
reviewers, the Associate Editor and the Editor.
References
Aiken, G. R. (1992), Chloride interference in the analysis of dissolved
organic carbon by the wet oxidation method, Environ. Sci. Technol.,
26, 2435–2439, doi:10.1021/es00036a015.
Aiken, G. R., E. M. Thurman, R. L. Malcolm, and H. F. Walton (1979),
Comparison of XAD macroporous resins for the concentration of fulvicacid from aqueous solution, Anal. Chem., 51, 1799–1803, doi:10.1021/
ac50047a044.
Aiken, G. R., D. M. McKnight, K. A. Thorn, and E. M. Thurman (1992),
Isolation of hydrophobic organic-acids from water using nonionic macroporous resins, Org. Geochem., 18, 567–573, doi:10.1016/0146-6380(92)
90119-I.
Aiken, G. R., H. Hsu-Kim, and J. N. Ryan (2011), Influence of dissolved
organic matter on the environmental fate of metals, nanoparticles and colloids, Environ. Sci. Technol., 45, 3196–3201, doi:10.1021/es103992s.
Alling, V., et al. (2010), Nonconservative behavior of dissolved organic
carbon across the Laptev and East Siberian Seas, Global Biogeochem.
Cycles, 24, GB4033, doi:10.1029/2010GB003834.
Baker, A., L. Bolton, M. Newson, and R. G. M. Spencer (2008), Spectrophotometric properties of surface water dissolved organic matter in an
afforested upland peat catchment, Hydrol. Processes, 22, 2325–2336,
doi:10.1002/hyp.6827.
Battin, T. J., L. A. Kaplan, S. Findlay, C. S. Hopkinson, E. Marti, A. I.
Packman, J. D. Newbold, and F. Sabater (2008), Biophysical controls
on organic carbon fluxes in fluvial networks, Nat. Geosci., 1, 95–100,
doi:10.1038/ngeo101.
Bergamaschi, B. A., et al. (2011), Methyl mercury dynamics in a tidal wetland quantified using in situ optical measurements, Limnol. Oceanogr.,
56, 1355–1371, doi:10.4319/lo.2011.56.4.1355.
Bernardes, M. C., et al. (2004), Riverine organic matter composition as a function of land use changes, southwest Amazon, Ecol. Appl., 14, 263–279,
doi:10.1890/01-6028.
Biddanda, B. A., and J. B. Cotner (2003), Dual effect of sunlight on the bioavailability of limnetic dissolved organic matter and its role in the aquatic
carbon cycle, J. Great Lakes Res., 29, 228–241, doi:10.1016/S0380-1330
(03)70429-8.
Blough, N. V., and R. Del Vecchio (2002), Chromophoric DOM in the coastal
environment, in Biogeochemistry of Marine Dissolved Organic Matter,
edited by D. A. Hansell and C. A. Carlson, pp. 509–546, Academic,
San Diego, doi:10.1016/B978-012323841-2/50012-9.
Blough, N. V., and S. A. Green (1995), Spectroscopic characterization
and remote sensing of NLOM, in The Role of Non-Living Organic Matter
in the Earth’s Carbon Cycle, edited by R. G. Zepp and C. Sonntag,
pp. 23–45, John Wiley and Sons, NYC.
Boyle, E. S., N. Guerriero, A. Thiallet, R. Del Vecchio, and N. V. Blough
(2009), Optical properties of humic substances and CDOM: Relation to
structure, Environ. Sci. Technol., 43, 2262–2268, doi:10.1021/es803264g.
Chow, A. T., R. A. Dahlgren, and J. A. Harrison (2007), Watershed sources
of disinfection byproduct precursors in the Sacramento and San Joaquin
Rivers, California, Environ. Sci. Technol., 41, 7645–7652, doi:10.1021/
es070621t.
Cole, J. J., et al. (2007), Plumbing the global carbon cycle: Integrating
inland waters into the terrestrial carbon budget, Ecosystems (N. Y.), 10,
172–185, doi:10.1007/s10021-006-9013-8.
Cory, R. M., D. M. McKnight, Y. P. Chin, P. Miller, and C. L. Jaros (2007),
Chemical characteristics of fulvic acids from Arctic surface waters:
Microbial contributions and photochemical transformations, J. Geophys.
Res., 112, G04S51, doi:10.1029/2006JG000343.
Cory, R. M., M. P. Miller, D. M. McKnight, J. J. Guerard, and P. L. Miller
(2010), Effect of instrument-specific response on the analysis of fulvic
acid fluorescence spectra, Limnol. Oceanogr. Methods, 8, 67–78,
doi:10.4319/lom.2010.8.0067.
Cotner, J. B., B. A. Biddanda, W. Makino, and E. Stets (2004), Organic carbon biogeochemistry of Lake Superior, Aquat. Ecosyst. Health Manage.,
7, 451–464, doi:10.1080/14634980490513292.
Crump, B. C., B. J. Peterson, P. A. Raymond, R. M. W. Amon, A. Rinehart,
J. W. McClelland, and R. M. Holmes (2009), Circumpolar synchrony
in big river bacterioplankton, Proc. Natl. Acad. Sci. U. S. A., 106,
21,208–21,212, doi:10.1073/pnas.0906149106.
Del Vecchio, R., and N. V. Blough (2004), On the origin of the optical
properties of humic substances, Environ. Sci. Technol., 38, 3885–3891,
doi:10.1021/es049912h.
Dittman, J. A., J. B. Shanley, C. T. Driscoll, G. R. Aiken, A. T. Chalmers, and
J. E. Towse (2009), Ultraviolet absorbance as a proxy for total dissolved
12 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
mercury in streams, Environ. Pollut., 157, 1953–1956, doi:10.1016/j.
envpol.2009.01.031.
Downing, B. D., E. Boss, B. A. Bergamaschi, J. A. Fleck, M. A. Lionberger,
N. K. Ganju, D. H. Schoellhamer, and R. Fujii (2009), Quantifying fluxes
and characterizing compositional changes of dissolved organic matter in
aquatic systems in situ using combined acoustic and optical measurements,
Limnol. Oceanogr. Methods, 7, 119–131, doi:10.4319/lom.2009.7.119.
Fellman, J. B., E. Hood, R. T. Edwards, and D. V. D’Amore (2009),
Changes in the concentration, biodegradability, and fluorescent properties
of dissolved organic matter during stormflows in coastal temperate watersheds, J. Geophys. Res., 114, G01021, doi:10.1029/2008JG000790.
Ferrari, G. M., M. D. Dowell, S. Grossi, and C. Targa (1996), Relationship
between the optical properties of chromophoric dissolved organic matter
and total concentration of dissolved organic carbon in the southern Baltic
Sea region, Mar. Chem., 55, 299–316, doi:10.1016/S0304-4203(96)
00061-8.
Griffin, C. G., K. E. Frey, J. Rogan, and R. M. Holmes (2011), Spatial and
interannual variability of dissolved organic matter in the Kolyma River,
East Siberia, observed using satellite imagery, J. Geophys. Res., 116,
G03018, doi:10.1029/2010JG001634.
Hedges, J. I., R. G. Keil, and R. Benner (1997), What happens to terrestrial
organic matter in the ocean? Org. Geochem., 27, 195–212, doi:10.1016/
S0146-6380(97)00066-1.
Helms, J. R., A. Stubbins, J. D. Ritchie, E. C. Minor, D. J. Kieber, and
K. Mopper (2008), Absorption spectral slopes, and slope ratios as indicators of molecular weight, source, and photobleaching of chromophoric
dissolved organic matter, Limnol. Oceanogr., 53, 955–969, doi:10.4319/
lo.2008.53.3.0955.
Hernes, P. J., and R. Benner (2003), Photochemical and microbial degradation
of dissolved lignin phenols: Implications for the fate of terrigenous dissolved
organic matter in marine environments, J. Geophys. Res., 108(C9), 3291,
doi:10.1029/2002JC001421.
Hiriart-Baer, V. P., N. Diep, and R. E. H. Smith (2008), Dissolved organic
matter in the Great Lakes: Role and nature of allochthonous material,
J. Great Lakes Res., 34, 383–394, doi:10.3394/0380-1330(2008)34
[383:DOMITG]2.0.CO;2.
Holmes, R. M., J. W. McClelland, P. A. Raymond, B. B. Frazer, B. J.
Peterson, and M. Stieglitz (2008), Lability of DOC transported by
Alaskan rivers to the Arctic Ocean, Geophys. Res. Lett., 35, L03402,
doi:10.1029/2007GL032837.
Hood, E., M. N. Gooseff, and S. L. Johnson (2006), Changes in the character of stream water dissolved organic carbon during flushing in three
small watersheds, Oregon, J. Geophys. Res., 111, G01007, doi:10.1029/
2005JG000082.
Hu, C. M., F. E. Muller-Karger, and R. G. Zepp (2002), Absorbance,
absorption coefficient, and apparent quantum yield: A comment on common ambiguity in the use of these optical concepts, Limnol. Oceanogr.,
47, 1261–1267.
Inamdar, S. P., N. O’Leary, M. J. Mitchell, and J. T. Riley (2006), The
impact of storm events on solute exports from a glaciated forested watershed in western New York, USA, Hydrol. Processes, 20, 3423–3439,
doi:10.1002/hyp.6141.
Jaffé, R., D. McKnight, N. Maie, R. Cory, W. H. McDowell, and J. L.
Campbell (2008), Spatial and temporal variations in DOM composition
in ecosystems: The importance of long-term monitoring of optical properties, J. Geophys. Res., 113, G04032, doi:10.1029/2008JG000683.
Krupa, M., R. G. M. Spencer, K. W. Tate, J. Six, C. van Kessel, and B. A.
Linquist (2012), Controls on dissolved organic carbon composition and
export from rice-dominated systems, Biogeochemistry, 108, 447–466,
doi:10.1007/s10533-011-9610-2.
Mann, P. J., A. Davydova, N. Zimov, R. G. M. Spencer, S. Davydov,
E. Bulygina, S. Zimov, and R. M. Holmes (2012), Controls on the composition and lability of dissolved organic matter in Siberia’s Kolyma River
Basin, J. Geophys. Res., 117, G01028, doi:10.1029/2011JG001798.
Mannino, A., M. E. Russ, and S. B. Hooker (2008), Algorithm development
and validation for satellite-derived distributions of DOC and CDOM in
the U.S. Middle Atlantic Bight, J. Geophys. Res., 113, C07051,
doi:10.1029/2007JC004493.
McKnight, D. M., and G. R. Aiken (1998), Sources and age of aquatic
humus, in Aquatic Humic Substances, edited by D. Hessen and L. Tranvik,
pp. 9–39, Springer, Berlin.
Moran, M. A., W. M. Sheldon, and R. G. Zepp (2000), Carbon loss and
optical property changes during long-term photochemical and biological
degradation of estuarine dissolved organic matter, Limnol. Oceanogr.,
45, 1254–1264, doi:10.4319/lo.2000.45.6.1254.
Mulholland, P. J. (2003), Large-scale patterns in dissolved organic carbon concentration, flux, and sources, in Aquatic Ecosystems Interactivity of Dissolved Organic Matter, edited by S. E. G. Findlay and R. L. Sinsabaugh,
G03001
pp. 139–159, Academic, San Diego, doi:10.1016/B978-012256371-3/
50007-X.
Obernosterer, I., and R. Benner (2004), Competition between biological and
photochemical processes in the mineralization of dissolved organic carbon, Limnol. Oceanogr., 49, 117–124, doi:10.4319/lo.2004.49.1.0117.
Opsahl, S. P., and R. G. Zepp (2001), Photochemically induced alteration of
stable carbon isotope ratios (d 13C) in terrigenous dissolved organic carbon, Geophys. Res. Lett., 28, 2417–2420, doi:10.1029/2000GL012686.
Osburn, C. L., and C. A. Stedmon (2011), Linking the chemical and optical
properties of dissolved organic matter in the Baltic-North Sea transition zone
to differentiate three allochthonous inputs, Mar. Chem., 126, 281–294,
doi:10.1016/j.marchem.2011.06.007.
Osburn, C. L., C. R. Wigdahl, S. C. Fritz, and J. E. Saros (2011), Dissolved
organic matter composition and photoreactivity in prairie lakes of the U.S.
Great Plains, Limnol. Oceanogr., 56, 2371–2390, doi:10.4319/lo.2011.
56.6.2371.
Pellerin, B. A., J. F. Saraceno, J. B. Shanley, S. D. Sebestyen, G. R. Aiken,
W. M. Wollheim, and B. A. Bergamaschi (2012), Taking the pulse of
snowmelt: In situ sensors reveal seasonal, event and diurnal patterns of
nitrate and dissolved organic matter variability in an upland forest stream,
Biogeochemistry, 108, 183–198, doi:10.1007/s10533-011-9589-8.
Peuravuori, J., and K. Pihlaja (1997), Molecular size distribution and spectroscopic properties of aquatic humic substances, Anal. Chim. Acta, 337,
133–149, doi:10.1016/S0003-2670(96)00412-6.
Rochelle-Newall, E. J., and T. R. Fisher (2002), Chromophoric dissolved
organic matter and dissolved organic carbon in Chesapeake Bay, Mar.
Chem., 77, 23–41, doi:10.1016/S0304-4203(01)00073-1.
Saraceno, J. F., B. A. Pellerin, B. D. Downing, E. Boss, P. A. M. Bachand,
and B. A. Bergamaschi (2009), High-frequency in situ optical measurements during a storm event: Assessing relationships between dissolved
organic matter, sediment concentrations, and hydrologic processes,
J. Geophys. Res., 114, G00F09, doi:10.1029/2009JG000989.
Schuster, P. F., J. B. Shanley, M. Marvin-Dipasquale, M. M. Reddy, G. R.
Aiken, D. A. Roth, H. E. Taylor, D. P. Krabbenhoft, and J. F. DeWild
(2008), Mercury and organic carbon dynamics during runoff episodes from
a northeastern USA watershed, Water Air Soil Pollut., 187, 89–108,
doi:10.1007/s11270-007-9500-3.
Shank, G. C., and A. Evans (2011), Distribution and photoreactivity of
chromophoric dissolved organic matter in northern Gulf of Mexico shelf
waters, Cont. Shelf Res., 31, 1128–1139, doi:10.1016/j.csr.2011.04.009.
Spencer, R. G. M., B. A. Pellerin, B. A. Bergamascui, B. D. Downing, T. E.
C. Kraus, D. R. Smart, R. A. Dahlgren, and P. J. Hernes (2007), Diurnal
variability in riverine dissolved organic matter composition determined
by in-situ optical measurement in the San Joaquin River (California,
U.S.A.), Hydrol. Processes, 21, 3181–3189, doi:10.1002/hyp.6887.
Spencer, R. G. M., G. R. Aiken, K. P. Wickland, R. G. Striegl, and P. J.
Hernes (2008), Seasonal and spatial variability in dissolved organic matter quantity and composition from the Yukon River basin, Alaska, Global
Biogeochem. Cycles, 22, GB4002, doi:10.1029/2008GB003231.
Spencer, R. G. M., G. R. Aiken, K. D. Butler, M. M. Dornblaser, R. G.
Striegl, and P. J. Hernes (2009a), Utilizing chromophoric dissolved
organic matter measurements to derive export and reactivity of dissolved
organic carbon to the Arctic Ocean: A case study of the Yukon River,
Alaska, Geophys. Res. Lett., 36, L06401, doi:10.1029/2008GL036831.
Spencer, R. G. M., et al. (2009b), Photochemical degradation of dissolved
organic matter and dissolved lignin phenols from the Congo River,
J. Geophys. Res., 114, G03010, doi:10.1029/2009JG000968.
Spencer, R. G. M., P. J. Hernes, R. Ruf, A. Baker, R. Y. Dyda, A. Stubbins,
and J. Six (2010a), Temporal controls on dissolved organic matter and
lignin biogeochemistry in a pristine tropical river, Democratic Republic
of Congo, J. Geophys. Res., 115, G03013, doi:10.1029/2009JG001180.
Spencer, R. G. M., G. R. Aiken, R. Y. Dyda, K. D. Butler, B. A. Bergamaschi,
and P. J. Hernes (2010b), Comparison of XAD with other dissolved lignin
isolation techniques and a compilation of analytical improvements for
the analysis of lignin in aquatic settings, Org. Geochem., 41, 445–453,
doi:10.1016/j.orggeochem.2010.02.004.
Stedmon, C. A., and S. Markager (2001), The optics of chromophoric dissolved organic matter (CDOM) in the Greenland Sea: An algorithm for
differentiation between marine and terrestrially derived organic matter,
Limnol. Oceanogr., 46, 2087–2093, doi:10.4319/lo.2001.46.8.2087.
Stedmon, C. A., R. M. W. Amon, A. J. Rinehart, and S. A. Walker (2011),
The supply and characteristics of colored dissolved organic matter
(CDOM) in the Arctic Ocean: Pan Arctic trends and differences, Mar.
Chem., 124, 108–118, doi:10.1016/j.marchem.2010.12.007.
Sterner, R. W. (2010), In situ-measured primary production in Lake Superior, J. Great Lakes Res., 36, 139–149, doi:10.1016/j.jglr.2009.12.007.
Stubbins, A., V. Hubbard, G. Uher, C. S. Law, R. C. Upstill-Goddard, G. R.
Aiken, and K. Mopper (2008), Relating carbon monoxide photoproduction
13 of 14
G03001
SPENCER ET AL.: DISSOLVED ORGANIC MATTER IN U.S. RIVERS
to dissolved organic matter functionality, Environ. Sci. Technol., 42,
3271–3276, doi:10.1021/es703014q.
Stubbins, A., R. G. M. Spencer, H. Chen, P. G. Hatcher, K. Mopper, P. J.
Hernes, V. L. Mwamba, A. M. Mangangu, J. N. Wabakanghanzi, and
J. Six (2010), Illuminated darkness: Molecular signatures of Congo River
dissolved organic matter and its photochemical alteration as revealed
by ultrahigh precision mass spectrometry, Limnol. Oceanogr., 55,
1467–1477, doi:10.4319/lo.2010.55.4.1467.
Valett, H. M., M. A. Baker, J. A. Morrice, C. S. Crawford, M. C. Molles,
C. N. Dahm, D. L. Moyer, J. R. Thibault, and L. M. Ellis (2005),
G03001
Biogeochemical and metabolic responses to the flood pulse in a semiarid
floodplain, Ecology, 86, 220–234, doi:10.1890/03-4091.
Vodacek, A., N. V. Blough, M. D. DeGrandpre, E. T. Peltzer, and R. K.
Nelson (1997), Seasonal variation of CDOM and DOC in the Middle
Atlantic bight: Terrestrial inputs and photooxidation, Limnol. Oceanogr.,
42, 674–686, doi:10.4319/lo.1997.42.4.0674.
Weishaar, J. L., G. R. Aiken, B. A. Bergamaschi, M. S. Fram, R. Fuji, and
K. Mopper (2003), Evaluation of specific ultraviolet absorbance as an
indicator of the chemical composition and reactivity of dissolved organic
carbon, Environ. Sci. Technol., 37, 4702–4708, doi:10.1021/es030360x.
14 of 14