Elevated phosphate (PO4) concentrations can harm the
ecological status in water by eutrophication. In the majority of surface
waters in lowland regions such as Flanders (Belgium), the local PO4
levels exceed the limits defined by environmental policy and fail to
decrease, despite decreasing total phosphorus (P) emissions. In order to
underpin the definition of current limits, this study was set up to
identify the pre-industrial background PO4 concentration in surface
water of the Scheldt River, a tidal river in Flanders. We used the
sedimentary records preserved in tidal marsh sediment cores as an archive
for reconstructing historical changes in surface water PO4. For
sediment samples at sequential depths below the sediment surface, we dated
the time of sediment deposition and analysed the extractable sediment P. The
resulting time series of sediment P was linked to the time series of
measured surface water-PO4 concentrations (data 1967–present). By
combining those datasets, the sorption characteristics of the sediment could
be described using a Langmuir-type sorption model. The calibrated sorption
model allowed us to estimate a pre-industrial background surface water
PO4 levels, based on deeper sediment P that stabilized at
concentrations smaller than the modern. In three out of the four cores, the
sediment P peaked around 1980, coinciding with the surface water PO4.
The estimated pre-industrial (∼1800) background
PO4 concentration in the Scheldt River water was 62 [57; 66 (95 % CI)] µgPO4-PL-1. That concentration exceeds the previously
estimated natural background values in Flanders (15–35 µgTPL-1) and is about half of the prevailing limit in the Scheldt River
(120 µgPO4-PL-1). In the 1930s, river water
concentrations were estimated at 140 [128; 148] µgPO4-PL-1, already exceeding the current limit. The method developed here
proved useful for reconstructing historical background PO4
concentrations of a lowland tidal river. A similar approach can apply to
other lowland tidal rivers to provide a scientific basis for local
catchment-specific PO4 backgrounds.
Introduction
Excess phosphorus (P) concentrations in surface waters is a global problem
(Azevedo
et al., 2015; Dodds and Smith, 2016; Elser et al., 2007). Eutrophication by
excess nutrients, including phosphate (PO4) and nitrogen (N), can lead to
hypoxia, acidification, and harmful algal blooms
(Azevedo
et al., 2015; Correll, 1998; Watson et al., 2018). Therefore, limiting P
concentrations in the surface water is crucial to ensure a good ecological
status. Lowland river systems are at higher risk for eutrophication than
upland streams (Watson et al.,
2018). As a result, eutrophication of lowland rivers is on the international
agenda
(Jarvie
et al., 2006; Mainstone and Parr, 2002; Reynolds, 2000). This study focuses
on dissolved orthophosphate (PO4), almost identical to the reactive P
determined by a colour reaction. Other P forms present in surface water
include organic P fractions and P adsorbed to mineral colloids. Total P
(TP) refers to all P forms together. The environmental limits for P are expressed as reactive P (equated to PO4-P limits), TP limits,
or both.
The lowland rivers of densely populated regions do not achieve good water
quality mainly due to the excess of nutrients
(Bitschofsky
and Nausch, 2019; Huet, 1990; Van Der Molen et al., 1998; Van Puijenbroek et
al., 2014; Rönspieß et al., 2020; Schulz and Herzog, 2004). For
example, since 2004, the average PO4 concentration in Flemish waterways
has stabilized at 290 µgPO4-PL-1. That concentration is
well above the limits varying between 70–140 µgPO4-PL-1 for
different river types (Smolders et al., 2017; VMM, 2018). Despite the
current net-zero P balance in agricultural soils in that region, the
situation has not improved since 2004 (VMM, 2017). The question
arises when or even whether these limits can be achieved.
Since 2000, the European Union has regulated surface water quality with the
Water Framework Directive 2000/60/EC (WFD), which does not prescribe limits
but provides a framework for local regulations. The WFD has identified a
high ecological status of a river if nutrient conditions remain within the
range associated with undisturbed conditions, i.e natural background levels
(European Commission, 2000). However, the definition of the
natural background has been subject to debate for many river basins
(Matschullat et al.,
2000; van Raaphorst et al., 2000). The natural background can be defined as
the situation (concentration range) found in the environment without any
human activity, reflecting only natural geochemical processes
(Laane, 1992; Reimann and Garrett,
2005). This definition implies that concentrations must be estimated before
human activity, which is not always feasible. Therefore, a pre-industrial
background can be defined instead, inferred from samples dating before the
industrial revolution (Reimann and Garrett,
2005). The pre-industrial background can logically be affected by
anthropogenic processes. Alternatively, natural background concentrations
can be estimated by sampling regions with an expected minimal anthropogenic
influence, i.e. reference lakes and rivers
(Cardoso et al., 2007).
Natural background concentrations have been established for different
chemical elements in rivers in Europe; however that did not include TP or
PO4-P (Salminen et al.,
2005).
For P in densely populated regions such as Flanders, the natural background
concentrations can only be inferred indirectly. Natural background TP
concentrations for Flanders were set at 15–35 µgPL-1, inferred from reference lakes sampled in central and Baltic states in
Europe (Cardoso et al.,
2007). However, the natural background PO4-P concentrations of lowland
rivers may be higher than those of upland rivers. For example, diatom
assemblages revealed natural eutrophic conditions in the Spree River in
Germany with TP concentrations of 80 µgL-1, compared to recent
data of 120 µgTPL-1 (Schönfelder and
Steinberg, 2004; Zak et al., 2006).
Lowland rivers can be significantly loaded with P derived from the riverbed
sediment, defined as internal loading (Froelich, 1988). The
internal loading of P better explains temporal and spatial trends in river P
concentrations than the P emissions of the lowland river system of Flanders
(Smolders et al., 2017).
Biogeochemical processes in the sediment explain that result. Ferric iron
(Fe(III)) and aluminium oxyhydroxides have a high affinity for
PO4 anions and limit the PO4 in solution
(Borggaard et al., 1990; Holtan et al.,
1988). However, anoxic conditions lead to the reductive dissolution of those
Fe(III) minerals, releasing the associated P to the overlying water when the
sediment is strongly reduced
(Baken
et al., 2015; van Dael et al., 2020). The small rivers and ditches in
lowland regions have a low water velocity and are nutrient-rich, leading to
anoxic conditions during summer and autumn. Those anoxic events explain the
typical summer peaks in PO4 in small rivers. Moreover, regional
differences in sediment P / Fe concentration ratios explain regional
differences in surface water PO4 concentrations
(Smolders et al., 2017).
Sediment analysis is linked to surface water P and can be a valuable tool
for assessing historical river water quality. In surface waters, sediments
can serve as a sink or a source of PO4, depending on the sediment
surface chemistry and water concentrations
(Froelich,
1988; House and Denison, 1998; Simpson et al., 2021; van der Zee et al.,
2007). For example, P storage on fine bed sediments can amount to 60 % of
a catchment nutrient budget
(Ballantine et al., 2009;
Svendsen and Kronvang, 1993). The essential processes for PO4 are
adsorption and desorption from Fe oxyhydroxides, present in the suspended
matter or bed sediments
(Froelich,
1988; van Raaphorst and Kloosterhuis, 1994; van der Zee et al., 2007).
Sediment P concentrations can likely predict surface water P concentrations
and have been relevant for the long-term reconstruction of P in the
environment
(Wang
et al., 2009; Zhou et al., 2005). For example, Boyle et al. (2015) used P
profiles from lake sediments in the UK to infer catchment P inputs over the
last 10 000 years and linked that to the historical evolution in population
density. Similarly, banded iron formations in deep oceanic waters allowed the
inference of oceanic P concentrations of over 2 billion years ago
(Bjerrum and Canfield, 2002). Likewise, the
sediments deposited by rivers or lakes react with surface water PO4 and
are deposited in regularly flooded areas. Thus, those sediments can serve as
an archive for reconstructing historical P emissions trends and provide
useful information on historical P concentrations in adjacent water bodies
(Birch et al., 2008).
In lowland rivers with tidal influence, like the Scheldt, vegetated tidal
marshes develop along the river banks. Tidal marshes directly adjacent to
tidal rivers are regularly flooded during high tides: these flooding events
deposit sediments and associated elements like P on the densely vegetated
marsh surface
(Friedrichs
and Perry, 2001; De Swart and Zimmerman, 2009; Temmerman et al., 2004a). The
net accumulation of sediments increases the elevation of tidal marshes over
time (Temmerman et al., 2003a).
Therefore, researchers have used tidal marshes as sediment archives of
deposited substances other than P, such as organic carbon
(Van de Broek et al.,
2019) and silicon (Struyf et al.,
2007). However, it remains to be investigated to what extent P
concentrations measured in tidal marsh sediment archives can be used to
reconstruct historical changes in PO4 concentrations in the adjacent
estuary.
This study tested and evaluated a methodology to estimate the pre-industrial
background water PO4 concentrations based on the analysis of tidal
marsh sediment. Those sediments had been deposited over multiple centuries
on the banks of Flanders' largest tidal river, the Scheldt. Using sediment
analysis and a sorption model provided the first estimate of pre-industrial
PO4 levels in a large lowland river. First, we described the tidal
marsh sediment sorption characteristics by linking the P concentration of
tidal marsh sediments to historical measurements of PO4 in the Scheldt
River water. Those sorption characteristics allowed an estimation of
historical river water PO4 concentrations. This estimate was based on
an analysis of sediments deposited in the 1800s or before industrialization.
The underlying assumption is that sediment P remains immobile and that the
sediment's depth profile reflects the historical trend of PO4 in the
Scheldt River. Accordingly, we argue that the sediment P composition in
deeper sediment layers of tidal marshes provides an archive of the historic
PO4 concentration of the adjacent river. A database containing
measurements of the PO4 concentration in the Scheldt River's surface
water (1967–current) verified this assumption. This study hypothesizes that
the previously estimated natural background P of this major lowland river is
larger than that estimated earlier for lakes (15–35 µgPL-1).
Materials and methodsStudy area
Freshwater tidal marshes were sampled at four locations along the Scheldt
River (Fig. 1 and Table S1 in the Supplement). The Scheldt estuary is located in northern Belgium
and the southwestern Netherlands and flows into the North Sea. The river
basin of the Scheldt covers a large part of Flanders (71 %) and the
adjacent region of northern France; the total catchment area is
approximately 22 000 km2. The population living in the river basin is
about 10 million (Meire et al., 2005). The tidal
wave extends from the mouth (Vlissingen) to 160 km upstream near Ghent,
where sluices stop the tidal wave. The estuary's freshwater tidal zone
reaches from Ghent to Rupelmonde (Fig. 1). This research focused on
freshwater tidal marshes, i.e. located in this freshwater tidal zone of the
estuary. Brackish waters experience the mixing of seawater, making it
difficult to distinguish the anthropogenic sources from seawater influence.
Furthermore, saltwater in the North Sea has PO4 concentrations about a
factor of 10 lower than fresh water in the Scheldt River
(Burson et al., 2016). This research was
focused on freshwater lowland river systems and the human influence on the
P concentrations; saltwater environments were beyond the scope of this
study.
Map of the Scheldt estuary: triangles indicate the locations of
the sampled tidal marshes. Old1 and Young1 were only 250 m apart, and on the
scale of the map, they overlap.
Sediment accreting in tidal marshes originates from the deposition of
riverine suspended matter, including inorganic mineral sediment and organic
matter (Callaway et al., 1996). We
discriminate between old and young tidal marshes, hereafter referred to as
marshes. Old marshes have a higher elevation compared to young marshes. As a
general mechanism, young marsh surfaces accumulate sediments quickly and
increase their elevation asymptotically up to an equilibrium level, which is
around the mean high water level
(MHWL) (Pethick,
1981; Temmerman et al., 2003a). Temmerman et al. (2003a) defined an old
marsh as visible on topographic maps of Ferraris (1774–1777), so it was
formed before the 19th century
(Temmerman et al., 2003a).
Young marshes in the Scheldt estuary were formed more recently, by the
natural establishment of pioneer marsh vegetation on formerly bare tidal
mudflats, generally after 1944. During the last decades, the young marshes
had a surface elevation below MHWL. As a result, young marshes experienced
more frequent inundations and had larger sediment accretion rates than old
marshes. For example, between 1931 and 1951, young marshes accumulated at
rates of 1.6 to 3.2 cmyr-1. In contrast, the elevation of old marshes
was very close to the yearly MHWL increase rate of 0.3 to 0.6 cmyr-1 in the western Scheldt
(Temmerman et al., 2003a).
This study analysed depth profiles of sediment cores originating from tidal
marshes along the freshwater Scheldt River. The analysis contained two old
and two young marshes, Old1, Old2, Young1, and Young2 (locations indicated in
Fig. 1). The coordinates of sampling locations can be found in
Van de Broek et al.
(2018, 2016) and Sect. SI.I in the Supplement.
Marshes Old1 and Young1 originated from the tidal marsh named the Notelaer,
Old 2 from Grembergen, and Young2 from Mariekerke. Eight cores were analysed:
three replicate cores for both sites Old1 and Young 1 and one core for Old2
and Young2.
PO4 concentration in surface waters
The Flanders Marine Institute (IMIS) provided surface water phosphate
(PO4) data measured colourimetrically on a filtered water sample and
total phosphorus (TP) by acid digestion and a segmented flow analyser.
Concentration data of PO4 in Scheldt River were available from 1967 to
2019, originating from different sources and compiled by the research
programme Onderzoek Milieu Effecten Sigmaplan (OMES). The OMES programme did
additional quality controls on the data (ECOBE – UA; The
Flemish Waterway, 2019). The different sources are described in
Sect. SI.V in the Supplement
(De Pauw, 2007; ECOBE – UAntwerpen, 2007; Institute for Hygiene en
Epidemiology (IHE), 2007; ECOBE-UA and De Vlaamse Waterweg, 2016;
Van Meel, 1958).
The open-source software R (R Core Team, 2020) was used to compile
all available datasets for PO4 closest to the study sites (Temse) and
to calculate annual means by averaging all observations within a year. The
annual means of PO4 were used to visualize the evolution of PO4 in
the Scheldt River (Fig. 2). The emissions of P mainly originate from point
sources due to domestic loading (Billen et al.,
2005). As a result, the increasing surface water P concentration between
1950 and 1975 can be related to the rise in the number of households
connected to sewer systems. At first, no wastewater treatment was in place,
resulting in a sharp increase in nutrient loads to the river. However, since
1985, wastewater treatment has significantly improved the situation
(Billen et al., 2005).
Concentrations of phosphate in the Scheldt River at Temse, annual
means and standard deviation (error bar) around the annual mean. Samples
were taken in Temse close to tidal marsh sites (data sources:
ECOBE-UA and De Vlaamse Waterweg,
2016; ECOBE – UAntwerpen, 2007; Institute voor Hygiëne en Epidemiologie
(IHE), 2007; Van Meel, 1958; De Pauw, 2007).
Sediment sampling
The sediment samples used here had been collected during a previous study
about carbon sequestration in tidal marsh sediments in the Scheldt estuary
(Van de Broek et al.,
2018, 2016). Collection of undisturbed sediment
profiles on the tidal marshes took place between July and September 2016
(Old1, Young1, Old2, Young2; Fig. 1). Undisturbed sediment cores were taken
at each sampling location using a gauge auger (0.06 m diameter). The cores
were divided into subsamples with a 0.03 m interval. The sediment samples
were dried at a maximum temperature of 50 ∘C for 48 h,
crushed, and sieved to a <2 mm grain size. Macroscopic vegetation
residues were removed manually using tweezers
(Van de Broek et al., 2018). Bulk density,
grain size distribution, and organic carbon (OC) content were analysed by Van
de Broek et al. (2018). We refer to Van de Broek et al. (2016, 2018) for further
sample collection and processing information.
Sediment analysis
The dried sediment samples were analysed for oxalate-extractable P, Fe, Al,
and Mn (Pox, Feox, Alox, Mnox;
Schwertmann, 1964). The preparation of extraction solution and dilutions
were made with ultrapure water (Milli-Q®), and all glassware
was acid-soaked overnight in a 1 % HCl acid bath to prevent P
contamination. That acid oxalate extractant, a mixture of ammonium oxalate
(0.2 M) and oxalic acid (0.2 M) at pH=3, targets poorly crystalline
oxyhydroxides of Fe, Al, and Mn and the associated P
(Schwertmann, 1964). Those poorly crystalline oxyhydroxides are
the most reactive due to their large specific surface area
(Hiemstra
et al., 2010). The extraction was done with 1 g of dry sediment in 50 mL
extraction solution over 2 h in an end-over-end shaker at
20 ∘C (26 rpm). The suspension was filtered through a
0.45 µm membrane filter (CHROMAFIL® Xtra PET – 45/25). Analytical
blanks, internal reference samples, and duplicate samples were included in
every batch to ensure the analysis's quality, purity, and reproducibility.
The extracts were diluted 20 times and measured by inductively coupled
plasma optical emission spectrometry (ICP-OES). The degree of P saturation
(DPS; %) was calculated as in Eq. (1). The DPS represents the extractable
(Pox) ratio to the P sorption capacity of the sediment. This P sorption
capacity is estimated as half of the sum of oxalate Feox and Alox,
because not all the Fe and Al in the soil are available for P sorption with
Feox, Alox, and Pox in molar units.
DPS=Pox0.5(Feox+Alox)100%
The DPS is expressed as a percentage and can be interpreted as the ratio of
sorption sites on the sediment occupied by P. Previous research used the DPS
to identify agricultural areas sensitive to phosphate leaching and showed a
good correlation with pore water P concentrations
(Breeuwsma
et al., 1995; Lookman et al., 1995; Schoumans and Chardon, 2015; Schoumans
and Groenendijk, 2000; van der Zee, 1988). The DPS relation for
porewater–soil systems was developed and verified by
van der Zee et al. (1990). Lexmond et al.
(1982) illustrated that the maximal sorbed P was about half the pool
available after a long-term precipitation experiment. Therefore, the factor
0.5 is an empirical value representing the soil's sorption capacity. The
parameter α primarily affects the maximum sorption capacity. So they
set α at 0.5±0.1. However, even among soils, this parameter
varied between 0.3 and 0.6 (Lexmond et al., 1982). For
this research, low background concentrations are most important, so
maximal sorption occurring at high PO4 concentrations is less relevant.
Age–depth model
The sediment analysis and the surface water PO4 data had to be linked
by a corresponding date and location to fit a sorption model. Therefore, an
age–depth model was used to calculate the time since deposition of each
sediment sample.
Temmerman
et al. (2004b, a) developed a time-stepping marsh sedimentation model
(MARSED). That model estimates sediment deposition rates and the resulting
evolution of the tidal marsh elevation in the Scheldt estuary. Hence, we
could use MARSED to determine the time since deposition of sediments
throughout the sampled sediment profiles. The MARSED model simulates the
tidal supply of suspended sediments and the settling to the marsh surface
during tidal inundation cycles integrated over the years. The model was
calibrated and validated against measured sediment deposition rates on the
Scheldt estuary tidal marshes from 1945 until 2002 (Temmerman et al., 2003a, 2004b). The empirical data on sediment deposition rates were derived from
radiometric and palaeoenvironmental dating of sediment cores at the exact
locations sampled for the present study
(Temmerman
et al., 2004a, b).
For our current study, we extrapolated the MARSED model simulations of
sediment accretion from 2002 until 2016, the sampling date of the sediment
cores (Van de Broek et al., 2018). However, simulations overestimated the
observed marsh surface elevation in 2016 by 25 cm for sampling location
Old1, 29 cm for Young1, 19 cm for Old2, and 8 cm for Young2 (observed by RTK
GPS surveying; Van de Broek et al., 2018; Poppelmonde, 2017). The
MARSED model was initially designed to simulate the overall sediment
accretion and surface elevation changes in tidal marshes in response to
sea-level rise scenarios, for which those errors were acceptable. In
contrast, the most important was the time of sediment deposition throughout
the sediment profile for the present study. Therefore, the original
age–depth relation calculated by MARSED was recalibrated using observed
age–depth points. The observed age–depth points originated from GPS
measurements of marsh elevation in 2016 (M. Van de Broek, unpublished data)
and previously published radiometric and palaeoenvironmental dating
(Temmerman et al., 2003, 2004). This rescaling procedure is explained in the
Supplement (Figs. S1–S4 in the Supplement).
An approximate extrapolation procedure was used to estimate the sediment
deposition time from depths below the oldest measured age–depth points
(mentioned in the previous sentence). The observed age–depth points were
available from 1958 for sampling site Old1, 1947 for Young1, 1963 for Old2,
and 1968 for Young2 (Temmerman et al., 2004). This extrapolation procedure
could only be applied for old marshes, which were defined as marshes that
existed at least since the end of the 18th century (Temmerman et al., 2003a, 2004). Two sediment cores originated from old tidal marshes (Old1 and
Old2). Based on observed age–depth points, it has been proven that the old
marshes reached equilibrium with the MHWL before 1944. After 1944, the old
marshes have built up their elevation at a rate comparable to local MHWL
rise (Temmerman et al., 2003a).
Here, we assumed that also between 1800 and 1944 these old marshes accreted
at a rate comparable to the MHWL rise.
Historical tide gauge data of MHWL rise were available from 1901 for site
Old1 and 1930 for site Old2
(ScheldeMonitor Team and
VNSC, 2020; Temmerman et al., 2003a), and linear regression of the MHWL
against time was used to estimate the marsh surface elevation before 1944
(Figs. S6 and S7 in the Supplement). However, the dating accuracy will be lower going further back
in time. Furthermore, such extrapolation to earlier dates is not appropriate
for young marshes, as they were only formed after 1950 by pioneer vegetation
establishment on formerly bare mudflats (Temmerman et al., 2003a, 2004).
Those mudflat sediment profiles do not have continuous sedimentary records
as tidal marshes and are likely to be disturbed by erosion and sedimentation
alternations (Belliard et
al., 2019). Therefore, the sediment deposition time could not be
extrapolated for the young marsh sampling locations.
Relating surface water PO4 with sediment P: the sediment–water model
The age–depth model and linear regression of MHWL provided a deposition year
for each sediment sample. Thereby, the dataset of water PO4 between
1967–2016 was linked to the sediment DPS for each core. The resulting
dataset contained all available surface water PO4 readings between
1967 and 2016, closest to the tidal marshes in Temse (n=1932) and a
corresponding DPS value. The DPS value of a sediment sample originates from
a specific layer of one sediment core or a mean DPS of the replicate
sediment samples. This dataset allowed us to fit a sorption model further
termed the sediment–water model. Schoumans and Groenendijk (2000) presented
a Langmuir-type sorption model to predict PO4 concentration leaching
from a soil layer based on the DPS Eq. (2).
[PO4]=K-1DPS100-DPS,
with [PO4] phosphate concentration in kgL-1, K the sorption
constant (Lkg-1), and DPS (degree of P saturation; %). This model
adequately described P sorption in soil across a wide range of pH values,
including the Scheldt River pH
(Schoumans
and Groenendijk, 2000; Warrinnier et al., 2018). The model relies on surface
complexation between PO4 and Fe and Al oxyhydroxides in the sediment.
That complexation is determined by a chemical equilibrium between solid
(adsorbed) and dissolved PO4 phase
(Warrinnier et al., 2019).
The parameter K of existing soil models has been calibrated for the soil–pore
water system, and the sediment–water parameter (K) is unlikely equal.
Therefore, the model was calibrated by fitting parameter K (Eq. 2) on
sediment DPS measurements and recent Scheldt water PO4 measurements. As
a result, the fitted K value is adapted to the local geochemistry of tidal
marsh sediments and the surface water.
We explored 16 different scenarios to fit the sediment–water model Eq. (2).
These scenarios illustrate the statistical uncertainty surrounding the
estimated PO4 concentrations. The model was fitted separately for each
site sediment core or on the combined replicate cores for Old1 and Young1
(Sect. SI.VI in the Supplement). Every sediment sample had between one and three replicates,
depending on the depth and the site. The average value of these replicates
was used or the individual replicate's DPS values. One sediment sample
covered several deposition years, so multiple PO4 observations
corresponded with each sediment sample. Again, the average of all
corresponding PO4 readings was taken, or all available values were used
separately. The combination of mean or individual DPS and PO4 resulted
in 16 models (Table S2 in the Supplement). For each of these, the parameter K was fitted by
non-linear least-squares regression with R using the Rstudio interface
(R Core Team, 2020; RStudio Team, 2015).
Evaluation of model performance
The predictions of the sediment–water model were evaluated based on several
parameters: the residual standard error (RSE), the Nash–Sutcliffe model
efficiency (E), and the measured surface water PO4 plotted against
predicted PO4 between 2007 and 2016 (Table S2 and Fig. S10 in the Supplement).
Additionally, the percentage bias (PBIAS) was calculated for data points
between 2007 and 2016. The PBIAS measures the average tendency of the
simulated data to be larger or smaller than their observed counterparts.
That difference is expressed as a percentage of deviation from the
observations (Moriasi et
al., 1983; Eq. 3). The predictions of recent years are interesting to
evaluate the model's performance for two reasons. First, the most recent
surface water PO4 concentrations are relatively low and more
representative of background concentrations. Second, the monitoring data
have a high temporal resolution, and the age–depth model is more accurate at
shallow depths.
PBIAS=∑i=1nYiobs-Yisim∑i=1nYiobs
ResultsHistory of surface water PO4 concentrations
The Scheldt PO4 concentrations varied greatly over the past decades,
with the peak in surface water PO4 concentrations between 1975 and 1985
(Fig. 2). In Temse, the annual mean concentrations rose from 410 µgPO4-PL-1 in 1967 and peaked in 1980 with 1570 µgPO4-PL-1. Between 1990 and 2003 concentrations decreased and
stabilized between 160 and 200 µgPO4-PL-1 in Temse. The
current PO4 levels are a factor of 2 lower than in 1967 and almost a
factor of 10 lower than the peak in 1980 (Fig. 2 and Table 1).
The sediment oxalate extractable P (Pox) and its degree of
phosphate saturation (DPS) of the top, bottom, and peak sediment layers at four
different tidal marsh locations. Top layers are the sediments closest to the
surface, peak layers had maximal Pox and DPS, and bottom layers are
those sediments sampled at the largest depth. Values of Pox and DPS are
means (± standard deviation) of N sediment samples, between top and
bottom (cm) depth.
The Pox in the sediments ranged between 370 and 13 000 mgPkg-1, while the DPS ranged between 13 % and 94 % (Table 1). In
all soil cores starting at the surface, the DPS and Pox increased with
depth and peaked at about 0.5 m below the surface (Figs. S7 and S8 in the Supplement). In
deeper (>1.0 m) sediment layers, Pox and DPS decreased and
stabilized for Old1, Young1, and Young2 (Table 1). Overall, the Pox
increased by an average factor of 3.5 between the surface and the maximum
concentrations (Fig. S8 and Table 1). The sediments with these peak DPS values were
deposited between 1960 and 1985 in three of the four sediment cores (Fig. 2). Only the core Old2 peaked earlier (ca. 1940–1950). Most importantly for
this work, DPS for Old1 showed an apparent stabilization in deeper or older
layers, which indicated undisturbed sediment layers (Figs. 3 and S7).
The degree of phosphate saturation (DPS) timeline based on four
tidal marsh sediment sites. Each dot represents a sediment analysis. The
year assigned to each sediment analysis was calculated with the age–depth
model. Before 1930, no model dates were available. Therefore a linear
regression of the MHWL was used to extrapolate the dates for the old
marshes. Dates before 1930 are increasingly uncertain going further back in
time. For young marshes, such extrapolation was not possible. The points
before the formation of the marshes are indicated with a question mark.
Within the first metre, Feox was stable in the three soil cores (Old1,
Young1, Old2) with concentrations around 20 000 mgkg-1, except for
Young2 for which Feox was a factor of 2 larger (Fig. S9 in the Supplement). For Young1 and
Young2, Feox concentration decreased at depths >1 m. For
Old1, Feox showed a steady decline from 20 000 mgkg-1 at the
surface to 10 000 mgkg-1 at the bottom of the profile (Fig. S9). The
Alox concentrations showed a similar trend as the Pox concentrations, with an initial increase followed by a decrease with
depth. The strong correlations of Alox and Feox with Pox
(rAl=0.73 and rFe=0.65) illustrate the positive effect of
Fe and Al oxyhydroxides on P sorption.
Sediment core selection
Under the assumption that PO4 does not migrate, the tidal marsh
sediment cores can provide an archive for river water PO4. Considering
P migration, it is crucial to evaluate the distance from a creek within the
tidal marsh. That distance is essential for two reasons. First, within 10 to
20 m of the creeks, the groundwater table fluctuates largely with the tides,
which can induce vertical P migration (Van Putte et al., 2020). Secondly,
sediment accretion is more difficult to predict at closer distances to the
creeks and can affect the age–depth relation (Temmerman et al., 2003b). The
distance from the sediment cores to the nearest creek was 21 m for Old1, 56 m for Young1, 35 m for Young2, and 5 m for Old2.
The assumption that PO4 does not migrate may be most violated at Old2
and Young2. The profile of Old2 indicated P migration because it had a peak
of Pox at an earlier date (1950) than was expected from surface water
data (1980) (Fig. 2). Consequently, Old2 was not taken up to interpret the
relation between DPS and PO4. For core Young2, deeper sediment layers
had a larger DPS than the surface layers (Table 2). Additionally, the age
estimation of sediments older than 1968 was impossible due to this tidal
marsh's young age. Furthermore, Feox concentrations were a factor of 2
larger than the other cores (Fig. S9) and a factor of 2 larger than the
average sediment Fe concentration of the upper Scheldt basin (VMM, 2019).
The local enrichment in iron lowers the DPS values and makes the core less
representative of the average situation in the Scheldt. These observations
made Young2 inappropriate to fit the relation between DPS and PO4.
The predicted concentrations of phosphate (PO4-P µgL-1) in the Scheldt River based on the degree of phosphate saturation
(DPS) in the sediment layers of marsh Old1, dating back to 1800
(pre-industrial), where DPS values stabilized with depth at 20 %. The
predicted concentration dated to 1930, where DPS stabilized at 36 %. The
PBIAS is the mean difference of simulated and observed data between 2007 and
2016, expressed as a percentage of the observed data. The conversion of DPS
to river phosphate concentration is based on the association of DPS with
PO4-P. That association was calibrated to data from 1967–2016, thereby using
different sediment–water models; the details of models are in Table S2.
Model 3b (in italics) is proposed as the most accurate (see text).
The two remaining soil cores, Old1 and Young1, originated from the same
tidal marsh area named “the Notelaer”, located near the city of Temse (Fig. 1). That marsh has been the topic of multiple studies on sediment accretion
(Temmerman et al., 2004b, 2003a) and soil OC stocks (Van de Broek et al.,
2018, 2016). The sediment profiles of both Old1
and Young1 sites rise and fall in DPS comparable to dynamics in surface water
PO4 concentrations (Figs. 2 and 3). In deeper sediment layers, DPS and
Pox stabilize below levels of recent deposits (Figs. S7 and S8). The time
series of Old1 displayed a DPS peak around 1960, indicating a shift of 20 years (Fig. 2). However, the core Old1 was taken up for the model fitting
because it dates back to 1800 at the deepest levels and is essential to
predict the background. Furthermore, the DPS concentrations stabilized
before 1920, indicating that P has not migrated to these depths, making it
suitable for background prediction. These observations suggested a
well-preserved Pox and DPS profile, essential for the DPS–PO4
relation. Therefore, Old1 and Young1 are considered the best profiles for
applying the sediment–water model and interpretation of background
concentrations.
Sediment–water model fit
The sediment–water model Eq. (2) was fitted on DPS–PO4 data from the
different sediment cores (Table S2). Two observations were omitted because
the DPS values were too large (0.99–1.02) and produced artefacts in the
results. The Nash–Sutcliffe model efficiency (E) ranged between 0.04 and
0.85 depending on the input data (Table S2; Nash and
Sutcliffe, 1970). The sediment–water model was fitted on each core's data
separately and combined data from Old1 and Young1, as they came from the
same tidal marsh location. The models fitted on data from sites Old2 and
Young2 were not considered as migration likely affected those cores (see Sect. 3.3).
The models fitted on an average DPS (across replicates) associated with
individual PO4 readings were considered most suitable (models 1b, 2b,
3b; Table S2). A single sediment sample analysis represents an average P
signal over the sediment's deposition period. However, the age–depth
relation can vary slightly due to the marsh surface elevation variation. By
taking an average DPS from replicate cores, the variation in the independent
variable was reduced. Furthermore, the prediction error increased in most
models by relating individual rather than mean DPS values with individual
PO4 measurements (Table S2). Models using unique DPS associated with
single PO4 data duplicated or even triplicated the PO4 data,
artificially creating more degrees of freedom (model 1c, 2c, 3c; Table S2).
Using mean PO4 values artificially reduced the degrees of freedom,
compromising the model predictions by increasing RSE and widening confidence
intervals (models 1a, 2a, 3a; Table S2). The fitted parameter K (Lkg-1) ranged between 1.0×106 and 5.4×106
for the different input datasets, with the 95 % confidence intervals
ranging between 0.8×106 and 7.2×106. The
variation in parameter K for the various input datasets was larger than the
individual confidence limit variation (Table S2). Thus, the uncertainty was
more pronounced due to the variability in sediment samples than due to the
model fit.
Model performance
The sediment–water model performance was evaluated with several parameters
(RSE, PBIAS, and E) and the actual PO4 by predicted plots of the PO4 concentrations over
the last decade. Those recent PO4 concentrations are more comparable
to the background (Fig. S10 in the Supplement and Table 2; mean Temse [2007–2016]=170µgPO4-PL-1). Model 3b was considered the most suitable for
predicting background concentrations. The PBIAS was the lowest for recent
observations for model 3b. The average tendency of simulated data compared
to the observations was only -14.9 %, which is within the acceptable
range of ±25 % (Moriasi
et al., 1983). Model 2b had an underestimation of observed data of more than
28 %, and model 1b overestimated recent observations by more than 60 %.
Such an overestimation is unwanted for calculating the background, and
therefore, both were considered unsuitable (Table 2). The actual PO4 by
predicted plots illustrates a similar message (Fig. S10). Based on these
observations, model 3b was considered the best model, although the residual
standard error (RSE) was lower for model 2b (Table S2). The selected model
3b successfully reconstructs the rise and fall in surface water
PO4 concentrations based on the sediment characteristic DPS (Fig. 4).
Measured (grey crosses; +) and predicted (black points)
PO4-P concentrations (µgL-1) in the Scheldt River in Temse.
The concentrations are calculated from the sediment phosphate saturation
(DPS) of the tidal marshes at Old1 and Young1, using sediment–water model
3b.
Maxima of monitored and predicted PO4 concentrations coincide in time
and have a similar size (Fig. 4). For example, in 1973, the average
PO4 concentration predicted by the model was 1200 µgPO4-PµgL-1, and measured concentrations were on average 1300 µgPO4-PµgL-1. The maximal predicted PO4 concentration
was 2200 µgPO4-PL-1, while the maximal observed concentration was 3000 µgPO4-PL-1. Predictions for recent years are within 15 %
of the observed data (e.g. 2015: model: 133 µgPO4-PL-1,
measured 155 µgPO4-PL-1). Between 1940 and 1990, the
modelled PO4 concentrations had more variation. Likewise, monitored
PO4 data are spread more between 1967 and 1990 (Fig. 2). Before 1930,
modelled PO4 concentrations stabilized at levels below current
observations (Fig. 4).
Estimating background PO4 concentrations in the Scheldt River
The deepest sediment layers are most suitable for predicting background
PO4 concentrations of the Scheldt River water. These layers are the
oldest and expected to have experienced the lowest impact of P additions
from anthropogenic sources. The Old1 marsh site was appropriate for this
purpose as it developed before 1774, before the industrial revolution in
Belgium. The average DPS for the bottom sediments, dated between 1800 and
1840, was 20 % for core Old1 (Table 1 and Fig. 2); these samples are
considered to represent the pre-industrial background. That DPS value
produced PO4 concentrations of 62 µgPO4-PL-1
[95 % CI (57; 66)] for the pre-industrial background, using sediment–water
model 3b (Table 2). The sediment dated to 1930 had a DPS of 36 %. For that
value, the same sediment–water model predicted a PO4 concentration of
140 µgPO4-PL-1 [95 % CI (128; 148)] (Table 2).
DiscussionPre-industrial background vs. ambient PO4 concentrations
This work presents a novel approach to reconstruct background surface water
PO4 concentration in a tidal river using the DPS of adjacent tidal
marsh sediments. The background concentration is essential in the context of
developing local nutrient limits. The predicted pre-industrial background
concentration (62 µgPO4-PL-1; Table 2) is about half of
the current surface limit of the Scheldt (120 µgPO4-PL-1;
Flemish Government, 1995). Remarkably, the predicted
background concentrations are about a factor of 2 larger than the background
estimates of lake waters for Flanders today (15–35 µgPO4-PL-1; Cardoso et al., 2007). That pre-industrial PO4 concentration
is about 3 times lower than the current concentration in the Scheldt.
For example, between 2007 and 2016, the mean PO4 concentration of the
Scheldt in Temse was 170 µgPO4-PL-1. However, in the
1930s, the concentration was estimated at 140 µgPO4-PL-1
and larger than current limits, at a time before widespread connection to
sewer systems, P-loaded detergents, and application of mineral fertilizers.
Those results suggest that the sediment internal loading triggered by summer
anoxia in lowland rivers contributes to larger PO4 concentrations than
estimated before (see introduction). The summer PO4 peak lasts about
5 months per year in Flanders and largely affects the rivers' mean P
concentrations (Smolders et al.,
2017). Summer anoxia can occur in eutrophic lakes or sometimes in
oligotrophic brown water lakes (Nürnberg, 1995).
Additionally, lowland rivers in Flanders are primarily groundwater-fed, and
73 % of streamflow can be attributed to base flow. The groundwater in
Belgium has a median P concentration between 150–320 µgPL-1 (Edmunds and Shand, 2009). Therefore, groundwater feeding
the river waters logically affects the river P concentrations. In contrast,
primarily rain-fed lakes will have lower P concentrations, with rain P
ranging between 1.5 and 120 µgPL-1 (Migon and Sandroni,
1999).
Limitations of the model
Care needs to be taken with background extrapolations to ensure that
post-depositional processes have not modified the biogeochemical patterns
and that the area represents the area of interest (Reimann and Garrett,
2005). Several factors can obscure the reconstructed background
concentrations. First, vertical migration of P can enrich deeper sediment
layers, causing an overestimation of the background. Second, the sediment
profiles at the tidal marshes are almost permanently saturated, so the
intrusion of P-rich groundwater could affect the P concentrations in the
tidal marsh sediment. Moreover, depending on the tidal marsh elevation,
periodic flooding occurs at an approximate range of 300–350 inundations per
year (Temmerman et al., 2003b). These
conditions could favour P migration due to the reductive dissolution of Fe
(oxy)hydroxides
(Baken
et al., 2015; van Dael et al., 2020).
Two cores with indications of PO4 migration were removed from the
analysis to address the issue (Old2 and Young2). These cores were identified
by the DPS age profile and considering the distance from the nearby creeks
(Figs. 2 and 1). Additionally, the DPS levels of the deepest sediment
layers were compared with layers at the surface. The surface layers had
lower DPS levels than the deepest layers for one core (Young 2). The two
remaining cores (Old1, Young1) had lower DPS levels in deeper sediment
layers (Fig. S7). More importantly, the modelled peak in PO4
concentrations based on the cores Old1 and Young1 were found within 2 years
of the monitored peak and had a similar magnitude (Fig. 4). The coinciding
peaks illustrate little migration of PO4 in Old1 and Young1, thereby
justifying these cores as an archive for water PO4.
The limited migration is also logical: at the average DPS of 90 % in
sediment showing at the peak, the sorption models predict that the
solid–liquid P concentration ratio is 2900 Lkg-1 with the average K
value of models of Table 2. That solid–liquid ratio can be converted to a
dimensionless retardation factor representing the ratio of the distance
migrated by the PO4 compared to the distance travelled by percolating
water. For example, the retardation was calculated to be 7500 with a bulk
density (ρb) of 1.3, porosity (θ) of 0.5, and a net vertical
annual water percolation of about 2 m. That retardation corresponds to
a net vertical P migration rate of 2.5 cm over 100 years, i.e. vanishingly
small (calculation details not shown).
Secondly, there is uncertainty on the age–depth estimation of the sampled
sediment profiles. The age–depth model is expected to be most reliable for
the Young1 sediment core, as it is based on a fitting of a modelled
age–depth relation to four observed age–depth points, while we only had two
observed age–depth points available for the other cores
(Temmerman et al.,
2004a). Additionally, observed age–depth points were not older than 1944.
Hence, the extrapolation of the age–depth model to periods before the older
available age–depth points is increasingly uncertain.
Pre-industrial and natural background values
The population increase between 1800 and 1930 can provide a first very
crude estimate of the population–DPS relation in the Scheldt basin. In 1800
the population in Belgium was around 3 million. Later, in 1930, this number
had more than doubled to 7 million (Vanhaute, 2003). A linear
relation between both suggests that the DPS is 8 % for the
pristine pre-anthropogenic environment, corresponding with a
PO4 concentration of 19–41 µgPO4-PL-1, i.e. close to
what researchers have indicated for pristine lakes. Such predictions need to
be corroborated with older sediment observations and other archaeological
information. The Scheldt River is logically more aerated than smaller
lowland rivers where summer anoxia is naturally more present; i.e. the
pristine PO4-P values will be higher.
Conclusions
Our study illustrated that tidal marsh sediments could evaluate
pre-industrial background PO4 concentrations of the freshwater rivers
like the Scheldt River. A sediment assessment can record time-integrated
environmental events, providing useful spatial and temporal information. Our
data estimated the pre-industrial background concentration at 62 µgPO4-PL-1 [95 % CI (57; 66)], about half of the environmental
limits set for surface waters in Flanders and neighbouring countries. Around
1930, the PO4 levels were only about 20 % lower than today, which is
a remarkably large concentration at a time before the massive application of
mineral fertilizers, with lower population density and limited connection to
sewer systems. The current PO4 concentrations decreased 10 times
from the peak found 40 years ago, reflecting wastewater treatment efforts
and reducing diffuse P emission. It is also clear from this study that the
pristine, pre-anthropogenic PO4-P concentrations in the Scheldt River
are well below the current ambient ones.
Data availability
The Supplement provides the sediment data analysis and age–depth model
results in csv format. In addition, results of surface water data are
available upon request at the IMIS (Flanders Marine Institute).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-763-2022-supplement.
Author contributions
FL, ES, and PC designed the research. FL conducted the investigation process
and developed the methodology under supervision of ES. MVDB carried out the
fieldwork and conceptualized the use of the samples. ST provided the
methodology for the age–depth model and software. TM validated the use of
the surface water data. EVM and FL placed the results in perspective with
historical data. All the authors contributed to discussion and data
interpretations, review, and editing of the work.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The results of this research greatly depended on the data
collected by the OMES-monitoring and The Flemish Waterway. Many years of
intensive data collection and quality assessment of the Scheldt River
resulted in a unique and valuable phosphate time series. We have the utmost
respect for their work and are thankful we could apply the dataset for this
research. We acknowledge Dries Grauwels and Kristin Coorevits for technical
assistance. We recognize the efforts from the anonymous reviewers for their
constructive comments on the work, which improved the quality of the result.
Finally, thanks to the Scheldt for providing this beautiful sediment archive
to travel back in time and explore environmental history.
Financial support
This research has been supported by the Research Foundation – Flanders (FWO), project identifier: G089319N.
Review statement
This paper was edited by Aninda Mazumdar and reviewed by two anonymous referees.
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