In aquatic ecosystems, the single-station, single-stage R diel
oxygen model assumes constant ecosystem respiration and aeration rate
(notwithstanding temperature effects) over the course of a single night. The
validity of this model was assessed for four small streams representing two
geologies (Chalk and Greensand) over a 1-year period, by examining the
behaviour of the nighttime dissolved oxygen (DO) saturation deficit for each
night at points where change in DO is zero. The resulting value was then
compared with the corresponding ratio (the regression quotient) obtained from
nighttime regression analysis (Hornberger and Kelly, 1975). If model
assumptions are correct, then these two values should be equal; where they
diverge therefore gives a method of assessing the suitability of the model
structure.
For two streams (one Chalk and one Greensand), the regression quotient
persistently underestimated the observed DO deficit. These two streams showed
similar timing patterns of oxygen dynamics with the point of minimum DO
occurring relatively quickly after sunset in spring and early summer,
although the two Chalk streams were more similar to one another in terms of
DO magnitudes. Comparisons between different streams using the single-station
model with constant R and k on the presumption that it is equally
appropriate in all cases may lead to misleading conclusions.
Introduction
The dissolved oxygen (DO) signal has been used to quantify primary productivity and respiration in aquatic ecosystems since the pioneering work of Odum (1956). Recently, the increased capacity to deploy automatic data loggers coupled with the ability to automate the analysis of the DO signal (e.g. Grace et al., 2015) has enabled the processing of potentially large amounts of data across multiple aquatic systems. Estimates of primary production obtained from the DO signal can then be used through the photosynthetic quotient (e.g. Duarte et al., 2010; Westlake, 1963) to estimate the corresponding carbon uptake. Therefore, with growing awareness of the significance of river systems in global carbon cycling (Cole et al., 2007; Wohl et al., 2017) it becomes more relevant to ensure both that the models used are sound and that model limitations are apparent.
Ecosystem metabolism
can be quantified by partitioning a single DO time series into its component fluxes, namely photosynthesis, ecosystem respiration and aeration. Although for
parts
of aquatic systems, oxygen consumption can be measured continuously, for example, through the use of benthic incubation chambers (e.g. Glud, 2008) or using eddy correlation techniques (e.g. Reimers et al., 2012), there is no method to measure oxygen consumption for the whole system. For aeration, although it is possible to measure the gas exchange constant using tracers such as sulfur hexafluoride (e.g. Beaulieu et al., 2013) or propane (e.g. Demars et al., 2011), from which the exchange constant for oxygen can be derived, only recently has a method been proposed (Pennington et al., 2018) to do this on a continuous basis. This means that time series estimates of oxygen consumption for a whole stream are coupled to estimates of the aeration flux and must be inferred, rather than measured, from DO time series, so that quantification of each depends on simultaneously quantifying the other.
There is experimental evidence that ecosystem respiration changes over a
single diurnal cycle (Staehr et al., 2010; Sadro et al., 2014; Alnoee et al.,
2014). However, for modelling purposes, both community respiration (R) and
the volumetric aeration rate constant (k) are typically assumed to be
constant (notwithstanding temperature effects) over one diurnal cycle (e.g.
Correa-Gonzalez et al., 2014; Izagirre et al., 2008; Benjamin et al., 2016;
Richmond et al., 2016). Appling et al. (2018) justify the use of a simple
model on grounds of parsimony that simple (i.e. single-stage R models) are
more resistant to overfitting, and Song et al. (2016) state that changes in
DO concentration can generally be described by single-stage R models. On
the other hand, Schindler et al. (2017) suggest that R is better
represented by a two-stage process according to whether the carbon source is
autochthonous or allochthonous and state that “The two-stage model fit oxygen
data considerably better than a single-stage model in nine of
13 stream × date combinations we considered”. Therefore, there is a
question as to what extent single-stage R models adequately describe DO
dynamics.
The open channel diel method requires the partitioning of the
stream-dissolved oxygen response into the dominant processes as described by
the following (single-stage R) equation (disregarding effects of
temperature on kinetics as they are not the focus of this research):
d(DO)dt=P-R+k(DOsat-DO),
where DO is dissolved oxygen concentration (g O2 m-3), P is
the oxygen flux resulting from photosynthesis
(g O2 m-3 s-1), -R is the oxygen consumption resulting
from aerobic respiration (g O2 m-3 s-1),
DOsat is dissolved oxygen concentration at saturation
(g O2 m-3), t is the time (s) and k is volumetric aeration
rate constant (s-1).
For nighttime, this relationship simplifies to the following:
d(DO)dt=-R+k(DOsat-DO).
Therefore, when d(DO)dt=0,
Rk=(DOsat-DO).
Therefore, if the model structure adequately captures DO dynamics,
at points of zero DO change in the nighttime DO time series the ratio of respiration to the volumetric aeration rate constant is equal to the observed oxygen saturation deficit. Thus, by identifying points in time of zero DO change, (DOsat-DO) can be observed from which the ratio Rk can be inferred.
These observations can then be compared with theoretical counterparts by using the nighttime regression method (Hornberger and Kelly, 1975) to obtain values of respiration (RHK) and k (kHK) and by extension the quotient (RHKkHK), hereafter referred to as the regression quotient.
The questions addressed are as follows:
How does the observed oxygen saturation deficit at points of zero DO change (DODzeroΔDO) behave over time?
How do nighttime DODzeroΔDO values (as proxies for Rk) compare with the regression quotient (RHKkHK)?
Does the time at which DODzeroΔDO occurs depend on the underlying stream geology?
MethodsStudy area
The study was conducted in the southern part of Britain in the Hampshire Avon
catchment. The catchment covers an area of 1706 km2 (NFRA, 2018) and
has an average annual rainfall of 810 mm. Approximately 80 % of the
catchment is arable or grassland and less than 2 % is urban. The dominant
geology in the catchment is highly permeable Chalk so that the rivers are
primarily groundwater fed. Instrumentation was located on four tributaries
within that catchment, the rivers Ebble, Wylye, Nadder and Upper Avon
(Table ) with surface water catchment sizes between 35 and
59 km2 and two dominant geology types (Chalk and Greensand). A more
detailed site description is available in Heppell et al. (2017).
Site location and catchment characteristics.
RiverMajor geologyLatitudeLongitudeCatchmentBFIMean flow (m-3 s-1)size (km2)(Jul 2014 to Jun 2015)EbbleChalk51.028-1.92458.90.9060.60WylyeChalk51.143-2.20353.50.901NANadderGreensand51.045-2.11034.60.7810.40AvonGreensand51.319-1.86259.20.7440.45
BFI: base flow index. Sources: Heppell et al. (2017) and for flow
data, Heppell and Binley (2016). NA: not available.
Instrumentation and data analysis
Dissolved oxygen and temperature were logged continuously using miniDOT data
loggers (Precision Measurement Engineering, Inc.) at a resolution of
0.01 mg L-1 and logging frequency of 1 min from mid-August 2014 to
mid-August 2015. The DO time series for the miniDOTs was smoothed using a
30 min time step with the change in DO (ΔDO) at each minute computed
from the smoothed time series. From this, the time at which ΔDO=0 was identified and the associated value of the DO deficit
was noted. Dissolved oxygen at saturation was calculated using tables
provided by United States Geological Survey (USGS, 2015) in accordance with
Standard Methods of the American Public Health Association (1998), using both
water temperature and atmospheric pressure, with atmospheric pressure data
provided by British Atmospheric Data Centre. For the nighttime regression
calculation, those data points that incorporated daytime values as a
consequence of the implemented moving average were excluded from the
regression. The data reported in this study is available from the NERC data
centre (Heppell and Parker, 2018).
DO time series for 5 to 20 May 2015 for two Chalk streams
(a) and two Greensand streams (b). Solid grey areas are the
nights of 9/10 and 16/17 May.
Results
Figure shows the DO time series (raw data) for a 2-week
period in May 2015. For the two Chalk rivers, daytime DO consistently rises
above DOsat typically by 1 to 3 mg DO per litre for the
Wylye and 1 to 2 mg DO per litre for the Ebble. For the Greensand rivers, the
Nadder rarely rises above saturation, and although the Avon does so,
nevertheless not as regularly as the two Chalk rivers. The Avon shows
anomalous behaviour for 13 and 14 May. Average daily DO maxima are 12.7,
12.2, 11.7 and 10.9 mg DO per litre for the Wylye, Ebble, Avon and Nadder
respectively, so that prima facie the Wylye is the most productive. Peak
daytime DO for the Wylye tends to happen later than that for the Ebble, as
does the peak for the Avon compared to the Nadder, so that for example in the
daytime of 16 May, DO for the Ebble and Wylye rises to 12 mg DO per litre,
after which DO in the Ebble declines whilst DO in the Wylye continues to rise
to 13.5 mg DO per litre. Note also that for the Ebble nighttime DO reaches a
minimum early each night, after which it rises throughout the night, whereas
the Wylye shows two types of behaviour, so that for example on the nights of
13/14 and 16/17 May, minimum DO occurs early whereas for 6/7 and 9/10 minimum
DO occurs much later in the night. This behaviour is summarised in
Fig. , with DO distributions shown in
Fig. a and DO expressed as percent saturation averaged by
time after sunrise shown in Fig. b. DO saturation levels
for the Ebble and Nadder typically plateau at just after solar noon, whereas
those for the Wylye and Avon continue to rise until 2 to 4 h after solar
noon. For nighttime, DO saturation levels for the Ebble and Nadder reach a
minimum relatively rapidly after sunset after which they increase slightly,
particularly the Nadder, whereas for the Avon they decline throughout the
night.
Distributions of DO values (a) and mean DO percent saturation by hours after sunrise (b) for 5 to 20 May 2015.
Analysis of lagged differences in DO between normalised DO time
series. (a) Ebble and Wylye, (b) Ebble and Nadder
(c) cross-correlations for four rivers for 5 to 20 May 2015.
In fact, the behaviour of the Ebble in terms of timing (i.e. phase) is much
closer to that of the Nadder than to the behaviour of the Wylye.
Figure (panels a and b) shows the distributions of
differences in DO at different lag intervals between normalised (that is,
mean DO is first subtracted) DO time series for the Wylye, Ebble and Nadder.
Each box plot is the distribution of the difference in DO for two of those
time series, with one time series having been time-shifted by the number of
minutes shown on the x axis. Of the two Chalk streams (Ebble and Wylye,
panel a), the Wylye tends to respond later than the Ebble and is
phase-shifted by approximately 90 min, whereas for the Ebble and Nadder
(panel b), both systems respond at approximately the same time. The
cross-correlations in Fig. c summarise the relative timings
for all four rivers; the correlation is stronger for the Ebble and Wylye and
for the Ebble and Nadder than it is for the Wylye compared to the Avon and
also the Nadder compared to the Avon (for which anomalous data of 13 and
14 May was removed prior to analysis). Nevertheless, for the whole time
series, the Avon lags the Nadder by approximately 2 h, which is consistent
with Fig. . Thus, in terms of typical DO magnitudes, the
two Chalk streams are similar (Fig. a), but in terms of
phase the Ebble is similar to the Nadder.
Time series for DO and ΔDO for the night of 9 to 10 May.
ΔDO is at 1 min intervals. Bold triangles mark those points where
there was a change in sign of ΔDO.
Time series for DO and ΔDO for the night of 16 to 17 May.
ΔDO is at 1 min intervals. Bold triangles mark those points where
there was a change in sign of ΔDO.
For the time series shown in Fig. , the nights of 9 to
10 May and of 16 to 17 May are shown as examples in
Figs. and
showing both raw DO data (grey circles) and a 30-point DO moving average
(solid black line), together with associated changes in DO at each minute.
The changes in DO are computed using the 30-point DO moving average, not
the raw data. Black triangles are those points where ΔDO=0, discussed further below. At sunset, the Wylye shows the greatest rate of
DO decline of -0.016 g O2 m-3 min-1. The Ebble and
the Nadder each experience approximately half that rate, with the Nadder
considerably less on 9/10 May. For the Avon, the initial rate of decline is
intermediate between those at about
-0.010 g O2 m-3 min-1. For all four rivers, the rate
of decline at sunset is higher on 9 than on 16 May. For the Ebble, there is a
saddle at approximately 1 h after sunset where there is a sudden drop in the
rate of decline. The main feature of the ΔDO plots, however, is the
difference in timing of the point at which ΔDO=0, where
for the Ebble and the Nadder it occurs between 1 and 3 h after sunset, for
the Avon between 6 and 8 h after sunset but for the Wylye on 9 May it occurs
late at 7.5 h after sunset and on 16 May it occurs early at 3 h after sunset.
Identification of the point at which there is zero change in DO is not as
straightforward as at first it seems; the change in DO for any 1 min time
step may be very close to, but never equal to, zero because of short-term
stochastic variability in the DO signal.
Identification could be achieved by fitting a line to the points in
Fig. and noting where the line crosses ΔDO=0, but this presupposes a particular model structure which may
be invalid. There are two other approaches, both of which have limitations
and both of which were implemented as a mutual check. One method (Method 1)
is to locate the point during the night at which DO is at a minimum. One
limitation is that there may be multiple local minima because of short-term
DO fluctuations, any of which could be the “true” global minimum for that
night. The main limitation, however, is that DO may decrease throughout the
night such that minimum DO occurs at the end of the night and ΔDO
itself is never equal to zero. Therefore, as a safeguard, in the
implementation of Method 1, if the minimum DO was found to occur within
20 min of sunrise, that outcome was discarded. The third approach (Method 2)
is to compare each pair of contiguous data points in the smoothed DO time
series and to identify those points where ΔDO changes sign (from negative to positive). These are
shown as black triangles in Figs. and
, which gives a range of
DODzeroΔDO values. For example, for the Wylye
for 16/17 May there are 11 data points where ΔDO changes sign from negative to positive, with
associated values of the DO deficit ranging between 3 and 3.18 mg DO per litre
with a median value of 3.06 occurring at 3 h and 9 min after sunset. For
the Ebble, corresponding numbers are 1.57 to 1.73 with a median of
1.7 mg DO per litre occurring at 2 h and 46 min after sunset. The median
value of those points can then be taken as the single value of the DO deficit
where ΔDO=0. The drawback of this approach is that there
may be anomalous data points (for example the Nadder in
Fig. ), which might yield erroneous
DODzeroΔDO values.
Box plot time series of DO deficits at points of steady-state DO for the nights of 9/10 and 16/17 May 2015 for Ebble and Nadder (a) and Wylye and Avon (b). Values for the regression quotient are
shown as triangles. Panel (c) shows corresponding distributions of
nighttime temperatures.
For the same two nights, the sets of DODzeroΔDO
values are shown as box plots (Fig. ). Also
shown (black triangles) are the corresponding values of the regression
quotient calculated from the nighttime regression method. For the Ebble and
the Nadder (panel a), the regression quotient underestimates the range of
DODzeroΔDO values. For the Avon (panel b), the
regression quotient slightly overestimates the median
DODzeroΔDO value for 9/10, but on 16/17 the
values are equal to one another.
For the Wylye (panel b), the regression quotient overestimates on 9/10 and
underestimates on 16/17. Thus, on the night when the DO minimum comes early
after sunset and the Wylye behaves more like the Ebble and the Nadder in
terms of timings of DO dynamics, the regression quotient underestimates the
median DODzeroΔDO value. Assuming constant R
and constant k, corresponding optimised simulations for 16/17 (using
“deSolve” and “FME” R libraries, Soetaert et al., 2010; Soetaert and
Petzoldt, 2010) for the Ebble and Avon are shown in
Fig. . The fit for both Ebble and Avon
appears to be good, but the residuals for the Ebble are highly
non-stationary. It is possible that these patterns arise because of a failure
to incorporate effects of temperature on reaction kinetics of R and k.
However, distributions of nighttime temperatures
(Fig. c) do not suggest that the Ebble and
Nadder have one temperature regime and the Wylye and Avon have another. In
fact, the temperature regimes for the Nadder and Avon are more similar to one
another than those for the Nadder and Ebble, even though the Nadder and Ebble
are the rivers with early DO nighttime minima, so temperature does not appear
to explain the differences in behaviour.
For data covering the entire study period (August 2014 to August 2015), the
distribution of the ratio of median DODzeroΔDO
values to the regression quotient is shown for each river in
Fig. . Where the ratio is greater than 1, the median
DODzeroΔDO exceeds the regression quotient. For
the Ebble and Nadder this is the case for about three-quarters of the nights
(75 % and 73 % for Ebble and Nadder respectively) whereas the
distributions are more symmetrical for the Wylye and Avon with corresponding
proportions of 60 % and 44 % respectively. Note also that groundwater
regimes may be similar, but oxygen regimes differ. So, for example, even
though the Wylye and Ebble are both Chalk and equally groundwater-dominated
with a BFI of 0.9 (Table ), a comparison of the distributions shown in Fig. suggests differing oxygen dynamics. A corresponding argument
applies to the Nadder and Avon.
A time series of median DODzeroΔDO values for the entire study period is shown in Fig. , together with a time series of the comparison with the regression quotient.
For the Ebble, median DODzeroΔDO values range
between 1 and 2.5 mg DO per litre with two peaks, one in
October/November 2014 and a second in summer 2015. A trough occurs in winter,
before rising to values in May similar to those in the previous September.
For the Wylye, values range between 2 and approximately 5 mg DO per litre; data
for June 2015 onward are more volatile and consequently less clear with
regard to any evident pattern. The seasonal pattern differs in that there is
no November 2014 peak, with an earlier autumnal peak occurring in
September 2014. Values for the Avon range between 2 and
4 mg DO per litre with peaks in October/November 2014 and a second in
June/July 2015. From mid-March to mid-April and again in late May/early June,
the median DODzeroΔDO for the Avon were
persistently low, with values of about 1 mg DO per litre or less. These points
were considered anomalous and were discarded from the analysis. For the
Nadder, the median DODzeroΔDO value rises
steadily from a value of 1.5 mg DO per litre at the beginning of March 2015 to
approximately 2.3 mg DO per litre in late June 2015. The Nadder differs from
the other three sites in that there is only one peak occurring between May
and September 2015, although the caveat is that data for the first part of
the time series is missing. None of the sites shows a marked difference in
behaviour according to whether Method 1 or Method 2 is used.
Also shown (Fig. ) is a comparison
between median DODzeroΔDO value (Rk)
and the regression quotient (RHKkHK),
expressed as the ratio of the former to the latter. For the Wylye and the
Avon, this ratio is very close to 1 over most of the year. For the Ebble and
the Nadder, however, the median DODzeroΔDO
values almost always exceed the regression quotient. The notable exception is
in October/November 2014 for the Ebble, where this pattern is reversed, with
the regression quotient tending to exceed median DODzeroΔDO values. Whether the regression quotient overestimates or
underestimates the median DODzeroΔDO value
depends partly on when median DODzeroΔDO
(Rk) occurs as shown for the Wylye in
Fig. ; if the change in sign of ΔDO occurs
relatively quickly after sunset (between 2 and 6 h after), then the
regression quotient is more likely to underestimate median
DODzeroΔDO and as the time after sunset
increases, the regression quotient has a tendency to overestimate
DODzeroΔDO values. As the time after sunset
further increases, the regression quotient again underestimates median
DODzeroΔDO. To demonstrate that this is not
simply a seasonal effect, this pattern is shown for the entire study period
(panel a) and also for the 2-month period up to 20 May 2015 (panel b).
Nighttime simulations for 16/17 May for Ebble and Avon. For Ebble, median DODzeroΔDO is 1.7 and regression quotient is 1.6, whereas for the Avon, they are equal (3.05).
Distributions of the ratio of DO deficit at points of zero DO change to the regression quotient.
Figure shows a time series for each river relating
to the length of time after sunset at which median DODzeroΔDO occurs. For September 2014 to February 2015, this interval is
notably variable for all rivers, ranging between 2 and 10 h. For May to
July 2015, the Ebble and Nadder show a clear pattern of a reduction in time
to DODzeroΔDO. For the Nadder, this remains
relatively constant at between 2 and 3 h. For the Ebble, DO reaches its
minimum point most quickly in May at approximately 3 h after sunset, but
then rises steadily through approximately 4 h in June, 5 h in July and 6 h
after sunset in August. For the Wylye, DODzeroΔDO in May and June 2015 occurs typically at just under 5 h after
sunset. Despite the fact that at other times of the year, the time interval
is more variable, nevertheless the annual pattern as indicated by the trend
line shows a clear periodicity with a maximum of approximately 10 h in
winter (November to January) for all rivers with river-specific patterns in
spring and summer.
Time series (2014–2015) of the DO deficit at points of zero DO
change (black circles) and comparison with corresponding ratio derived from
nighttime regression (grey crosses). Trend lines are shown for both time
series. For grey dashed line (Nadder), see text.
The regression quotient up to this point was computed using all data points
for any given night. An alternative would be to calculate the regression
quotient using only a subset of nighttime points. One possibility would be to
do so using only those data points clustered around the time after sunset at
which ΔDO=0. The effect of this is shown for the Nadder in
Fig. , which compares the regression quotient for each night
in the year, calculated using all data points for each night, with that
obtained using only those data points that are recorded within 15 min before
or after the time where ΔDO=0. By restricting the
nighttime regression calculation to those points, the bias is seen to be
removed. This does not necessarily mean that the associated estimates of R
and k are better, but it might mean that comparisons between nights are
more consistent, although this possibility was not investigated further.
Discussion
For four sites on four separate rivers, two Chalk (Wylye and Ebble) and two
Greensand (Avon and Nadder), DO data were analysed for the period August 2014
to August 2015 with particular focus on a 2-week period in May 2015. For each
night in the year, the nighttime dissolved oxygen deficit at points of zero
DO change (DODzeroΔDO) was identified and used
as a proxy for the ratio of community respiration to the volumetric aeration
rate constant. This ratio was compared to a theoretical equivalent, the
regression quotient, computed using the nighttime regression method
(Hornberger and Kelly, 1975). The objective in comparing these ratios was to
provide an aid in assessing the validity of assuming single-stage
respiration. When daily median DODzeroΔDO
values were compared to daily regression quotient values for the year as a
whole, the regression quotients for the Ebble and Nadder persistently
underestimated median DODzeroΔDO values.
Additionally, for the May period, although the two Chalk rivers were more
alike in terms of DO magnitudes, timings for the Ebble (times of daily DO
maxima and minima) were very close to those of the Nadder, with
DODzeroΔDO occurring relatively quickly after
sunset. For the year, using the Wylye as an exemplar, it was shown that the
regression quotient typically underestimates DODzeroΔDO when DODzeroΔDO occurs relatively
quickly after sunset.
Typically, single-station DO models assume constant R and k
(notwithstanding temperature effects) over the course of a single night. The
analysis set out above provides a method of assessing the extent to which the
assumptions of the single-stage R oxygen dynamics model are satisfied.
Assume both R and k are constant and given (at nighttime) that the following relationship holds:
d(DO)dt=-R+k(DOsat-DO),
then
A plot of ΔDO against (DOsat-DO) will give a straight line with slope k and constant term R. This is used to calculate a ratio R/k
(ratio 1).
and
At the point where ΔDO is zero, the oxygen saturation deficit (DOsat-DO) is measured. This gives a different method of calculating the same quantity, R/k (ratio 2).
If Eq. (1) adequately describes the nighttime DO dynamics, then ratio 1 will
be equal to ratio 2. If, however, they diverge significantly, then the
assumptions are not satisfied. For 16 May, for example, for the Ebble,
ratio 1 is 1.6 and ratio 2 is 1.7, but for the Avon, they are equal (3.05),
and the corresponding simulations (Fig. )
show clear differences in the pattern of residuals.
In itself, this divergence does not demonstrate that R is not constant, but
that model assumptions are not upheld. One other possibility is that k is
variable. Of course, k may differ across sites, but in order to violate
model assumptions it must change both within the course of a single night and
according to the same pattern for several nights in a row (as for the Ebble
in May 2015). Changes in discharge and wind speed could be expected to have
an impact on k. However, there is no difference in the discharge (data not
shown) that would account for differences between Ebble and Avon. It could be
that wind speed is changing every night in a consistent manner and therefore
k is changing, but changes in the wind speed would be similar across all
sites. Therefore, changes in wind speed could only account for the behaviour
if the Wylye and Avon were sheltered, and buffered from the effects of
changes in wind speed. However, wind speeds tend to drop during the night, so
that, if for the Ebble and Nadder, a variable k were explained by falling
wind speed, then one would expect DO to stagnate as the night progresses, but
the reverse is the case.
On the other hand, ecosystem respiration is known to change over a single
diurnal cycle (Staehr et al., 2010; Sadro et al., 2014; Alnoee et al., 2014).
Schindler et al. (2017) suggest that increases in nighttime oxygen
concentrations, as is the case for both the Ebble and the Nadder in May
(Figs. and ),
might be indicative of two-stage ecosystem metabolism. Increasing nighttime
DO could be brought about by falling water temperature alone, but nighttime
water temperature declines for all four sites, yet only the Ebble and Nadder
consistently register increases in nighttime DO percent saturation
(Fig. ). The fact that the Ebble and Wylye exhibit
similar DO ranges in May, yet the daytime Ebble DO peak typically occurs
earlier could indicate that for the Ebble, as photosynthesis progresses, some
products of that process are aerobically consumed. There is, however, an
important caveat. Of the four rivers, the Wylye records the highest DO
concentrations so that there is a prima facie case that the Wylye is the most
productive. Although there were nights during which the Wylye showed
increases in nighttime DO, it still consistently recorded the highest daytime
DO values in May 2015. Assuming that this is because the Wylye has the
highest primary production, that would mean that nighttime rises in DO maybe
sufficient, but not necessary, as indicators of productive aquatic systems.
Relationship between time after sunset until the point of zero DO
change and the ratio DODzeroΔDO: (regression
quotient) for the river Wylye for the entire study period (a) and
the 2-month period up to 20 May 2015 (b). Hours after sunset are
rounded to the nearest whole hour.
Time after sunset at which ΔDO is zero (2014–2015).
Scatter plots for regression quotient against median DO deficit at
points of zero DO change for the Nadder.
No part of the analysis presented above demonstrates, however, that R
varies over the course of a day, just that the single-stage R model
structure is less appropriate in some cases and that this is more likely to
be explained by a variable R than by a variable k. There may be other
confounding factors in the analysis, such as probe drift, but simulations
(supplementary analysis) incorporating an assumed probe drift did not alter
the conclusions. If the divergence is explained by other factors, this still
means that those other factors, whatever they may be, are not incorporated
into the model. The use of the single-stage R model to characterise or
quantify aspects of stream metabolism and DO dynamics is more appropriate for
some streams than others, so it is important to identify the correct model
for each river system and indiscriminate application of the single-stage R
diel oxygen model can result in misleading inferences when comparing
different sites.
Behaviour of DO dynamics were also examined with regard to hours after sunset at which ΔDO is zero. Typically, a DO time series will be presented with time marked as civil time in a particular time zone, but framing time in terms of the behaviour of the sun both makes inter-site comparisons more transparent and also is more pertinent to the response of the aquatic plant community. It also means that, by identifying as an annual time series, the time after sunset at which ΔDO is zero, anomalous behaviour can also be identified and used either as a filter to remove spurious data or as a flag to search for particular events, for example floods or periods of unusually low flow.
The regression quotient was calculated for the night as a whole and also by
restricting the data points included to those ±15 min either side of the point at
which change DO is zero. This was found to remove the bias. This does not
mean that calculations of R and k using only those points will give
better estimates of R and k, since if there is two-stage metabolism, then
such an approach would be disregarding the photosynthetic-dependent R,
although it might mean that intra-stream comparisons over a series of nights
are more consistent.
Conclusions
This paper began with a comment on the proliferation of automatic logging
devices which vastly increases the potential for analysis of river oxygen and
therefore river carbon dynamics. Oxygen dynamics are often analysed using
models that make simplifying assumptions about the underlying processes,
specifically about the constant values of both community aerobic respiration
and the reaeration rate constant over the course of a single day. However,
there is a debate about the extent to which respiration in particular can be
represented by a single daily value. Through analysis of the dissolved oxygen
deficit at points of zero DO change for four sites on four rivers, it was
shown here that the assumption of constant values for either respiration or
the aeration rate constant was violated perennially for two of those sites.
It was suggested that this is likely to be because of two-stage rather than
one-stage respiration, although it should be noted that variability in the
volumetric aeration rate or even unidentified factors could account for the
findings. In any case, this means that the use of single station,
single-stage respiration diel oxygen models might not be optimal in such
cases. This is not to decry the use of such models, as the purpose of a model
is to abstract from reality. However, if analysis of DO time series were to
become routine with results impacting environmental policy decisions, then it
would be important to understand when these models are failing rather than
presume that they are fit for purpose.
Data availability
Natural Environment Research Council (NERC) data at 10.5285/840228a7-40a1-4db4-aef0-a9fea2079987 (Heppell and Parker, 2018).
Competing interests
The author declares that there is no conflict of interest.
Acknowledgements
Foundation work was carried out as part of post-doctoral research at Imperial College London. Thanks to Adrian Butler for support.
Financial support
This research has been supported by the Natural Environment Research Council (grant nos. NE/J01219X/1 and NE/J012106/1).
Review statement
This paper was edited by Perran Cook and reviewed by two anonymous referees.
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