Characterizing and understanding spatial variability in
water quality for a variety of chemical elements is an issue for present and
future water resource management. However, most studies of spatial
variability in water quality focus on a single element and rarely consider
headwater catchments. Moreover, they assess few catchments and focus on
annual means without considering seasonal variations. To overcome these
limitations, we studied spatial variability and seasonal variation in
dissolved C, N, and P concentrations at the scale of an intensively farmed
region of France (Brittany). We analysed 185 headwater catchments (from
5–179 km2) for which 10-year time series of monthly
concentrations and daily stream flow were available from public databases.
We calculated interannual loads, concentration percentiles, and seasonal
metrics for each element to assess their spatial patterns and correlations.
We then performed rank correlation analyses between water quality, human
pressures, and soil and climate features. Results show that nitrate
(NO3) concentrations increased with increasing agricultural pressures
and base flow contribution; dissolved organic carbon (DOC) concentrations
decreased with increasing rainfall, base flow contribution, and topography;
and soluble reactive phosphorus (SRP) concentrations showed weaker positive
correlations with diffuse and point sources, rainfall and topography. An
opposite pattern was found between DOC and NO3: spatially, between
their median concentrations, and temporally, according to their seasonal
cycles. In addition, the quality of annual maximum NO3 concentration
was in phase with maximum flow when the base flow index was low, but this
synchrony disappeared when flow flashiness was lower. These DOC–NO3
seasonal cycle types were related to the mixing of flow paths combined with
the spatial variability of their respective sources and to local
biogeochemical processes. The annual maximum SRP concentration occurred
during the low-flow period in nearly all catchments. This likely resulted
from the dominance of P point sources. The approach shows that despite the
relatively low frequency of public water quality data, such databases can
provide consistent pictures of the spatio-temporal variability of water
quality and of its drivers as soon as they contain a large number of
catchments to compare and a sufficient length of concentration time series.
Introduction
As a condition for human health, food production, and ecosystem functions,
water quality is recognized as “one of the main challenges of the 21st
century” (FAO and WWC, 2015; UNESCO, 2015), and potential impacts of
climate change on water quality are even more challenging (Whitehead et al.,
2009). To better estimate and reduce human impact on water quality, water
scientists are expected to provide integrated understanding of multiple
pollutants (Cosgrove and Loucks, 2015). Eutrophication risks (Dodds and
Smith, 2016) are considered the main factors that decrease the quality of
surface water, according to objectives set by the European Union Water
Framework Directive. Mitigating the problem of eutrophication involves
considering at least the three major elements: carbon (C), nitrogen (N), and
phosphorus (P) (Le Moal et al., 2019).
In addition, the quality of headwater catchments has been studied less than
large rivers (Bishop et al., 2008), despite their influence on downstream
water quality (Alexander et al., 2007; Barnes and Raymond, 2010; Bol et al.,
2018) and higher spatial variability in their concentrations (Abbott et al.,
2018a; Temnerud and Bishop, 2005). One reason for this is that most water
quality monitoring networks coincide with the location of drinking-water
production facilities, which explains why they focus on large rivers.
Nonetheless, investigating spatial variability in upstream water quality is
relevant for understanding what causes it to degrade, targeting locations
with the greatest disturbances, and identifying which remediation policies
would be most cost-effective.
In non-agricultural headwater catchments, spatial variability in dissolved
organic C (DOC) concentrations in streams has been related to topography,
wetland coverage, and soil properties such as clay content or pH (Andersson
and Nyberg, 2008; Brooks et al., 1999; Creed et al., 2008; Hytteborn et al.,
2015; Musolff et al., 2018; Temnerud and Bishop, 2005; Zarnetske et al.,
2018). Stream DOC concentrations and composition in agricultural and
urbanized areas also generally differ greatly from those in semi-natural or
pristine catchments (Graeber et al., 2012; Gücker et al., 2016). Over
large gradients of human impact (e.g. from undisturbed to urban catchments),
the cover of agricultural and urban land uses often appears as a key factor
that explains differences in stream chemistry of C, N, and P species (e.g.
Barnes and Raymond, 2010; Edwards et al., 2000; Mutema et al., 2015) and
even silica (Onderka et al., 2012). Conversely, in mostly undisturbed
catchments (Mengistu et al., 2014) or in rural catchments where human
pressure are low (Heppell et al., 2017; Lintern et al., 2018), “natural”
controls such as topography, geology, and flow paths are more frequently
highlighted as the main factors that explain spatial variability in C, N, and
P.
Besides being spatially variable, C, N, and P concentrations also vary
temporally. The variability of concentrations with flow has been described
in several studies using concentration–flow relationships at event (Fasching
et al., 2019) or inter-annual to long-term scales (Basu et al., 2010, 2011;
Moatar et al., 2017). Concentrations also vary seasonally in streams and
rivers (Aubert et al., 2013; Dawson et al., 2008; Duncan et al., 2015;
Exner-Kittridge et al., 2016; Lambert et al.,
2013),
as does the composition of dissolved organic matter (Griffiths et al., 2011;
Gücker et al., 2016). This seasonality can also be spatially structured.
Several studies showed that the relative importance of catchment
characteristics on water concentrations or loads varied by season because
nutrient sources and biological and physico-chemical processes that
influence nutrient mobilization and transfer in catchments (e.g. vegetation
uptake, in-stream biomass production, denitrification) changed with the
hydrological, light, and temperature conditions (Ågren et al., 2007;
Fasching et al., 2016; Gardner and McGlynn, 2009). Some variability in
seasonal patterns of dissolved C, N, and/or P concentrations among headwater
catchments has been reported (e.g. Van Meter et al., 2019; Abbott et al.,
2018b; Duncan et al., 2015; Martin et al., 2004). Identifying these patterns
is relevant from a management viewpoint as they may indicate changes in the
locations of C, N, or P sources or their transfer pathways.
Thus, to date, analysis of spatial variability in water quality at the
headwater scale
is usually restricted to one element, although multi-element approaches are
becoming more frequent (Edwards et al., 2000; Heppell et al., 2017; Lintern
et al., 2018; Mengistu et al., 2014; Mutema et al., 2015).
is particularly rare for headwater catchments with similar human pressures
(e.g. intensive farming), despite the high variability in water quality
sometimes observed among them (e.g. Thomas et al., 2014).
often uses mean annual values (concentration or load) to describe spatial
variability in water quality among catchments, with little or no analysis of
seasonal patterns despite their frequent occurrence (Van Meter et al., 2019;
Abbott et al., 2018b; Liu et al., 2014; Halliday et al., 2012; Mullholland
and Hill, 1997).
is usually restricted to a few catchments: multiple-catchment studies on
multiple elements are uncommon, despite their ability to identify dominant
controlling factors better.
We studied the spatial variability and seasonal variation in water quality
of 185 headwater catchments (from 5–179 km2) draining
Brittany, an intensively farmed region of France. Our analysis focuses on
dissolved C, N, and P concentrations as DOC, nitrate (NO3), and soluble
reactive P (SRP), respectively. We hypothesized the following:
Human (i.e. rural and urban) pressures determine spatial variability in
NO3 and SRP concentrations (Preston et al., 2011; Melland et al., 2012;
Dupas et al., 2015a; Kaushal et al., 2018), while soil and climate
characteristics, including light and temperature along the stream, determine
that in DOC and possibly SRP (Lambert et al., 2013; Humbert et al., 2015; Gu
et al., 2017).
Seasonal variations in water quality provide information about spatial
variability in biogeochemical sources and/or reactivity in catchments as a
function of changes in water pathways and are correlated in part with
spatial variability in concentrations and loads.
We selected headwater catchments for which relevant time series of DOC,
NO3, and SRP concentrations and stream flow were available (10 years of
consecutive data measured at least monthly). In addition to estimating
interannual loads, we calculated concentration metrics for each element to
assess the spatial variability and temporal variation in water quality.
Generalized additive models (GAMs) were applied to the time series to
highlight average patterns of seasonal variation. Correlations between the
water quality metrics and the geological, soil, climatic, hydrological, land
cover, and human pressure characteristics of the corresponding headwater
catchments were evaluated using rank correlation analyses.
Materials and methodsStudy area
Brittany is a 27 208 km2 region in western France. Its
bedrock is composed mainly of a crystalline substratum dominated by granite
and schist (Supplement Fig. S1b). Its topography is moderate, with elevation
ranging from 0–330 m a.s.l. Its climate is temperate oceanic, with
precipitation ranging from 531 mm yr-1 in the east to 1070 mm yr-1
on the western coasts (regional median of 723.0 mm yr-1) (Fig. S1a), with a mean annual temperature of 12 ∘C. The regional
hydrographic network is dense, with a mean density of 1 km km-2. Overall, 56.6 % of the region was utilized
agricultural area (UAA) in 2017 (data from DREAL Bretagne, Brittany's Agency
for Environment, Infrastructure, and Housing), which represented 6 % of
national UAA in 2016. Of total French production, Brittany produces 17.4 %
of milk and dairy products, 20 % of pork products, and 17 % of eggs and
poultry (Chambres d'agriculture de Bretagne, 2016 data). At the canton
(administrative district) scale, mean N and P surpluses are high and have
high spatial variability (standard deviation (SD)): 50.01±26.59 kg N ha-1 yr-1 and 22.52±12.66 kg P ha-1 yr-1
(Fig. S1e, f). The region has a population of ca. 3.3 million
inhabitants (data 2017), some scattered throughout the region, and some
concentrated in a few cities and near the coasts (Fig. S1c, d).
Stream data selection and headwater characteristics
Water quality data consisted of time series of DOC, NO3, and SRP
concentrations, extracted from two public monitoring networks – OSUR
(Loire-Brittany Water Agency, 554 sites) and HYDRE/BEA (DREAL Bretagne, ca.
1964 sites), measured for regulatory monitoring, regional contracts, or
specific programmes. Concentrations were measured from grab samples. Headwater
catchments were selected according to the following two criteria: (i) independence, with no overlap of the drained areas of the water-quality
stations selected, and (ii) availability of at least 80 measurements of DOC,
NO3, and SRP concentrations at the same station (after removing
outliers based on expert knowledge, i.e. values > 200 mg N L-1 or 5 g P L-1) over 10 calendar years (2007–2016). We
selected 185 stations (83 % and 17 % from OSUR and HYDRE/BEA,
respectively) (hereafter, “concentration (C) stations”), which had mean
frequencies of 12, 14, and 11 analyses per year for DOC, NO3, and SRP,
respectively. We checked that there was no bias in the timing of
concentration data: OSUR database has fixed and regular sampling frequencies
while we noticed a few time series where summer periods were less sampled in
the HYDRE/BEA data for some years only.
Each C station was paired with a hydrometric station (Q). Observed daily
streamflow data from the national hydrometric network (http://www.hydro.eaufrance.fr/, last access: 28 April 2021)
were used when draining headwater catchments for C and Q stations shared at
least 80 % of their areas (25 % of cases). When observed Q data were not
available, or at a frequency less than 320 measurements per year from
2007–2016 (75 % of cases), discharge data were simulated using the GR4J
model (Perrin et al., 2003). The headwater catchments selected and their
associated C and Q stations were distributed throughout Brittany (Fig. 1).
Locations of the 185 study headwater catchments where dissolved
organic carbon, nitrate, and soluble reactive phosphorus concentrations were
monitored monthly at the outlet from 2007–2016, and paired discharge
stations where daily records of stream flow were available from observations
or modelling.
The 185 headwater catchments selected cover ca. 32 % of Brittany's area.
Despite having a similar hydrographic context dominated by subsurface flow,
the catchments have large differences in topography, geology, hydrology, and
diffuse and point-source pressures of N and P. We used a set of catchment
descriptors to quantify this variability (Table 1) (see Fig. S2 for
their statistical distribution and S3 for their correlations). The
descriptors selected included a set of spatial metrics for element sources
(e.g. land use, pressure, soil contents) and for mobilization and retention
processes (e.g. hydrology, climate, topography, geology, and soil
properties).
The headwater catchments range in area from 5–179 km2 (median
of 38 km2), and the density of each one's hydrographic
network ranges from 0.47–1.49 km km-2 (median of 0.90 km km-2). Strahler stream order is 3 for 36 % of the
catchments, 2 for 18 %, 4 for 17 %, and 1 for 11 %. Substrate
composition is dominated by schists/mica schists (44 %) or
granites/gneisses (31 %). In the topsoil horizon (0–30 cm), the soil
organic C content varies greatly from 18.6–565.4 g kg-1 (median of
126.9 g kg-1), while the total P (Dyer method) content varies from
0.6–1.4 g kg-1 (median of 0.9 g kg-1). Land use is largely
agricultural, although some catchments have high percentages of forested and
urbanized areas. Riparian wetlands cover 12.3 %–36.3 % of catchment area
(median of 22.4 %), forest covers 1.3 %–55.7 % (median of 13.2 %),
pasture covers 10.3 %–46.7 % (median of 25.6 %), summer crops cover
6.5 %–50.3 % (median of 27.8 %), and winter crops cover 7.0 %–51.0 %
(median of 22.7 %). The N and P surplus (potential diffuse agricultural
sources) varies from 12.9–96.0 kg N ha-1 yr-1 (median of 47.7) and
2.8–63.2 kg P ha-1 yr-1 (median of 18.9), respectively. Urban
areas cover 1.3 %–31.8 % of the headwater catchments (median of 6 %), with
point-source input estimates ranging from 0–6.2 kg N ha-1 yr-1
and 0–0.626 kg P ha-1 yr-1. These data illustrate relative
diversity in human pressures among the catchments despite a regional context
of intensive agriculture. The daily mean flow (Qmean) varies from 4.8–24.5 L s-1 km-2 (median of 10.8 L s-1 km-2), the median of annual minimum of
monthly flows (QMNA) varies from 0.2–5.9 L s-1 km-2, and the flow flashiness index (W2), defined as the percentage of total
discharge that occurs during the highest 2 % of flows (Moatar et al.,
2020), ranges from 10 %–28 %.
Data analysisConcentration and load metrics
To analyse spatial variability in DOC, NO3, and SRP concentrations in
streams, we calculated their 10th, 50th, and 90th percentiles
of concentration (C10, C50, and C90, respectively) for each headwater
catchment from 2007–2016. We also calculated the ratio of the coefficient of
variation (CV) of mean concentration (CVcmean) and to that of mean flow
(CVqmean) to compare spatial variabilities in concentrations and stream
flow. We estimated interannual loads for a 10-year period (2007–2016), with
8–12 C–Q values per year. However, a 5-year period (2010–2014) was
considered to analyse the spatial variability because it minimized data gaps
(in C and Q time series) among all stations simultaneously.
To calculate interannual DOC, NO3, and SRP loads for each headwater
catchment, we tested different methods and selected the most suitable,
depending on the reactivity of the element with flow. When C–Q relationships
were relatively flat or diluted (NO3) or slowly mobilized (DOC) during
high flow (Q>Q50), we used the discharge-weighted concentration
(DWC) method (Eq. 1), which estimates loads with lower uncertainties (Moatar
and Meybeck, 2007; Raymond et al., 2013):
DWC=kA×∑i=1nCiQi∑i=1nQiQ‾,
where DWC is the mean of annual loads (kg yr-1 ha-1), Ci is
the instantaneous concentration (mg L-1), Qi is the corresponding flow rate (m3 s-1), Q‾ is the mean annual flow rate calculated from daily data
(m3 s-1), A is the area of the headwater catchment
(ha), k is a conversion factor (31 536), and n is the
number of C–Q pairs per year.
The loads estimated by the DWC method were corrected for bias (Moatar et
al., 2013). Precisions were calculated from the number of samples (n),
number of years, export regime exponent (b50high), and W2 (Moatar et
al., 2020).
To calculate SRP loads, regression methods were more suitable (because of
strong concentration patterns when stream flow increases). We averaged the
loads estimated by two regression methods developed by Raymond et al. (2013)
- integral regression curve (IRC) and segmented regression curve (SRC) –
both based on a regression between concentration and flow:
2IRC=k′A×∑i=1nCiQi3SRC=k′A×∑i=1m1CinfiQi+∑i=1m2CsupiQi,
where IRC and SRC are the mean of annual loads (kg yr-1 ha-1);
Ci, Csupi, and
Cinfi are instantaneous concentrations estimated by
the regression curves (mg L-1); Csupi and
Cinfi are concentrations estimated for flows above
and below the median flow, respectively; n= 365 d; m1 and m2 are
numbers of days with daily flows below and above the median flow,
respectively; k′ is a conversion factor (86.4); and A is the area of the
headwater catchment (ha).
Seasonal signal
Seasonal dynamics of discharge and solute concentrations were modelled using
GAMs (Wood, 2017), which can estimate smoothed seasonal dynamics from time
series (Musolff et al., 2017). The smoothing function was a cyclic cubic
spline fitted to the month of the year (1–12); thus, the ends of the spline
were forced to be equal, using the R package mgcv. We did not consider a
long-term trend in the time series over the 10 years, for two reasons.
First, significant long-term trends (according to Mann–Kendall tests) had low
slopes: mean Theil–Sen slopes ranged from -3 % to 0 % of the median
concentration (while mean seasonal relative amplitudes exceeded 50 %).
Second, performance of the GAMs did not increase significantly when a
long-term trend was added: the mean-adjusted coefficient of determination
(Rsq) increased from 0.16 to 0.18 for DOC and from 0.30 to 0.40 for
NO3. We considered a seasonal dynamic to exist when the GAM-adjusted
coefficient of determination was greater than 0.10.
Seasonal dynamics of the concentrations of the three solutes (DOC, NO3,
and SRP) and river discharge were then analysed using five metrics
calculated from the daily simulations of the GAMs. The first three were the
annual amplitude (Ampli; i.e. annual maximum minus annual minimum), and the
mean time in which annual maximum and minimum concentrations occurred
(MaxPhase and MinPhase, respectively; in months from 1 January). The next
was Ampli standardized by the corresponding mean concentration to compare
the three solutes. The last metric was a seasonality index (SI), which
measures the relative importance of summer (1 June to 31 July)
concentrations compared to winter (15 January to 15 March) concentrations of
an element, as follows (Eq. 4):
SI=Cwinter-CsummerCwinter+Csummer,
where Cwinter and Csummer are
the averages of winter and summer concentrations, calculated from daily
values from fitted GAM. Positive values of SI (near 1) indicate that
Cwinter>Csummer, while negative values (near -1) indicate
that Cwinter<Csummer. We considered that SI values close to 0
(from -0.1 to 0.1) indicated that Cwinter equaled
Csummer. The SI integrates both amplitude and phasing
features of the seasonal signal. These five metrics, obtained from daily
simulations of the GAMs, are linked to geographical variables (Sect. 2.2), even
if particular solutes in some catchments do not present any seasonality.
Statistical analyses
To compare the concentration metrics of the elements, a multivariate
analytical approach, principal component analysis (PCA), was performed for
the nine variables of concentration percentiles (C10, C50, and C90) of DOC,
NO3, and SRP for the dataset of 185 headwater catchments. PCA was
chosen despite its assumption of linear relationships between variables,
because it provides a graphical representation of correlations between
variables or groups of variables and their contributions to the variance. To
identify dominant drivers of spatial variability in concentration
percentiles, seasonality, and loads of DOC, NO3, and SRP, we calculated
Spearman's rank correlation (rs) between these water-quality metrics
and the descriptors of the headwater catchments (Table 1). We considered a
rank correlation to be significant if the corresponding p value was ≤ 0.05. All analyses were performed using R software (v. 3.6.1) with packages
mgcv, hydroGOF, hydrostats, FactoMineR, tidyverse, lubridate, reshape2,
plyr, ggcorrplot, and ggplot2 (Grolemund and Wickham, 2011; Le et al., 2008;
Wickham, 2011, 2016; Wood, 2017; Zambrano-Bigiarini, 2020).
Headwater catchment descriptors identified as potential explanatory
variables of spatial variability and temporal variation in dissolved organic
carbon (DOC), nitrate (NO3), and soluble reactive phosphorus (SRP) in
stream and river water.
meanTWI=logαtanβ, where α is the drainage area (ha) and
β is the downstream slope (%) (Merot et al., 2003).
TypeDescriptor nameUnitDefinitionSourceTopographyAreakm2Drainage area of the monitoring stationWeb processing service “Service de Traitement de Modèles Numériques de Terrain” and DEM 50 m by IGNElevationmMean elevation of headwater catchmentDEM 25 m by IGNDensity_hnkm km-2Density of the hydrographic networkBD Carthage by IGNmeanTWIcf. legendAverage topographic wetness index of the headwater catchmentDEM 25 m by IGNIDPR–Hydrographic network development and persistence indexhttp://infoterre.brgm.fr/ (last access: 28 April 2021) BRGM data and geoservices portal (Mardhel and Gravier, 2004)GeologyGranite_pm%Percentage of granite and gneiss areaWeb mapping service “Carte des Sols de Bretagne” by UMR 1069 SAS INRAE – Agrocampus Ouest http://www.sols-de-bretagne.fr/ (last access: 28 April 2021)Schist_pm%Percentage of schist and mica schist areaOther_pm%Percentage of various geological substrataSoilErosion%Percentage of area with high to very high erosion risk (derived from land use, topography, and soil properties)Erosion risk map estimated from MESALES by GIS Sol, INRAE from Colmar et al. (2010)OC_soilg kg-1Organic carbon content in the topsoil horizon (0–30 cm)Web mapping service from BDAT database, Saby et al. (2015) by GIS SolThick_soilcmClasses of dominant soil thicknessaWeb mapping service “Carte des Sols de Bretagne” by UMR 1069 SAS INRAE – Agrocampus OuestTP_soilg kg-1Total phosphorus content in the topsoil horizon (0–30 cm)Web mapping service from BDAT database by GIS SolLand useSummerCrop%Percentage of summer cropb landOSO database, CESBIO, land-cover map 2016 (1 ha) from http://osr-cesbio.ups-tlse.fr/~oso/ (last access: 28 April 2021)WinterCrop%Percentage of winter cropc landForest%Percentage of forest land
Continued.
TypeDescriptor nameUnitDefinitionSourceLand usePasture%Percentage of pasture landWeb mapping service “Enveloppe des milieux potentiellement humides de France réalisée par les laboratoires Infosol et UMR SAS” by UMR 1069 SAS INRAE – Agrocampus Ouest/US 1106 InfoSol INRAEUrban%Percentage of urban landWetland%Percentage of potential wetlandsDiffuse and point N and P sourcesN_surpluskg ha-1 yr-1Nitrogen surplus (i.e. the maximum quantity on a given agricultural area that is likely to be transferred to the stream network)CASSIS-N estimates by Poisvert et al. (2017) from https://geosciences.univ-tours.fr/cassis/login (last access: 28 April 2021)P_surpluskg ha-1 yr-1Phosphorous surplusNOPOLU estimates by SoeS (2013)N_pointkg ha-1 yr-1Sum of nitrogen loads from domestic and industrial point sourcesData from Loire-Bretagne Water Agency data (2008–2012)P_pointkg ha-1 yr-1Sum of phosphorus loads from domestic and industrial point sourcesData from Loire-Bretagne Water Agency (2008–2012)HydrologyQmeanL s-1 km-2Interannual mean flowCalculated from flow data observations: HYDRO regional database by DREAL Bretagne & GR4J simulations (Perrin et al., 2003)QMNAL s-1 km-2Median of annual minimum monthly specific dischargeBFI%Base flow index (Lyne and Hollick, 1979)W2%Percentage of total discharge that occurs during the highest 2 % of flows (Moatar et al., 2013)Rainfallmm yr-1Mean effective rainfall from 2008-2012SAFRAN database (8 km2) by Météo France
a There are three classes of soil thickness: 40–60, 60–80, 80–100,
and > 100 cm. b Winter crops have a winter plant cover and a
phenological maximum in April (wheat, barley, rapeseed). c Summer crops
correspond to bare winter soils and a phenological maximum in early summer
(corn).
ResultsSpatial variability in concentrations and loads
The C50 of the 185 headwater catchments ranged from 2–14.6 mg C L-1 for
DOC, 0.9–15.8 mg N L-1 for NO3, and 8–241 µg P L-1
for SRP (with 75 % of the SRP C50< 64 µg P L-1). The
C50 displayed spatial gradients: rivers with DOC concentrations > 5 mg C L-1 were located in eastern Brittany, while the highest NO3
concentrations were located on the west coast (Fig. 2). In contrast, the
highest concentrations of SRP (C50> 68 µg P L-1)
were located in northern Brittany.
The two first axes of the PCA (Fig. 3a) performed on the percentiles of DOC,
NO3, and SRP concentrations of the 185 headwater catchments explained
58 % of the variance and revealed three important points. First,
percentiles (C10, C50, or C90) were grouped by solute, showing that the
spatial organization remained the same regardless of the concentration
percentile (Spearman rank correlations between the three indices always
greater than 0.56 for all elements). Second, there was a negative
correlation between the C50 of DOC and NO3 (rs=-0.58; Figs. 3b
and S3, S8). Third, SRP concentrations had an orthogonal
relation compared to DOC and NO3 concentrations (rs close to
zero).
The ratios of mean concentration (CVcmean) to mean flow (CVqmean)
were <1 for DOC and NO3 (Table 2), indicating that
concentrations varied less in space than in flow, and vice versa for SRP.
For DOC and NO3, Ampli was not correlated significantly with C50, but
it was with C90 (Figs. 4 and S8). For SRP, correlations between
Ampli and the percentiles were high, with rs> 0.85 for C50
and C90 (Figs. 4, S8). The SI and phases, calculated on the
catchments for which a GAM can be fitted, i.e. presenting a seasonal
feature, were correlated more with C10 for DOC (n=107) and NO3
(n=98) (negatively for SI and positively for the phases), and more with
C90 for SRP (n=118) (negatively, for SI only).
Mean (±1 SD) interannual loads had high spatial variabilities -20.71±10.52 kg C ha-1 yr-1 for DOC, 27.48±18.51 kg N ha-1 yr-1 for NO3, and 0.315±0.11 kg P ha-1 yr-1 for SRP – which differed from those observed for
concentrations (Fig. 2). Unsurprisingly, interannual loads of the three
solutes were significantly (p<0.001) and strongly correlated with
annual water fluxes (Pearson r= 0.88 for DOC, 0.90 for NO3, and 0.75
for SRP). There were weak but significant positive correlations between mean
interannual loads and seasonality indices (Ampli, SI) or C90 for DOC (Fig. 4). Mean interannual loads of NO3 were significantly and positively
correlated with C10 and C50, and negatively with its seasonality indices.
The strongest significant correlation was found between mean interannual
loads and concentration percentiles for SRP.
Map of median (a, c, e) concentrations C50 and (b, d, f) loads of
dissolved organic carbon (DOC), nitrate N (N-NO3), and soluble reactive
phosphorus (SRP) for the 185 streams. The catchments in grey did not meet
the criteria to estimate a mean average interannual load. Classes in the
legends have equal numbers of catchments.
(a) Principal component analysis of
10th, 50th, and
90th percentiles (C10, C50, and C90) of nitrate
(N-NO3), dissolved organic carbon (DOC), and soluble
reactive phosphorus (SRP) concentrations for the 185 headwater catchments
analysed. (b) Correlation between the medians (C50) of DOC and
N-NO3 concentrations for the 159 catchments in which
DOC and NO3 were monitored from 2007–2017. The colour
gradient indicates the percentage of catchment area covered by summer crops.
Matrices of Spearman's rank correlations of water quality
(load, concentration percentiles (10th (C10),
50th (C50), and 90th (C90)),
and seasonality metrics) for (a) dissolved organic carbon (DOC), (b) nitrate
N (N-NO3), and (c) soluble reactive
phosphorus (SRP) (c). Only significant (p≤0.05)
values are shown.
Coefficients of variation (spatial variability among
catchments) of flow-weighted mean concentration (CVcmean) and mean stream
flow (CVqmean), and the value of their ratio, for dissolved organic carbon
(DOC), nitrate (NO3), and soluble reactive phosphorus
(SRP).
ParameterCVcmeanCVqmeanCVcmean:CVqmeanDOC0.29540.46140.6403NO30.32850.47090.6976SRP0.92070.47431.9412Characterization of concentration seasonalityPerformance of GAMS
Of the 185 catchments, GAMs were fitted for 159 to DOC concentrations time
series, 168 to NO3 concentrations time series, 162 to SRP
concentrations time series, and 185 to discharge time series. The cases for
which fitting was not possible corresponded to those with no seasonal
cyclicity or with excessive interannual variability. The percentage of
variance explained by the GAM varied by site and solute. Fitting performed
best for NO3, followed by SRP and then DOC: the means and SDs of the
adjusted Rsq were 0.30±0.18, 0.16±0.11, and 0.22±0.15 for NO3, DOC, and SRP, respectively (Figs. S4 and S5), and
the percentages of catchment for which the fitted model had Rsq > 0.20 were 67 %, 52 % and 38 %, respectively. Metrics calculated from
monthly data differed only moderately from those calculated from sub-monthly
data (Fig. S6), which tended to validate the approach of using
monthly data.
Types of seasonal cyclicity in DOC, NO3, and SRP
Most of the catchments had a seasonal concentration cycle: 85 %, 71 %,
and 78 %, for NO3, DOC, and SRP concentrations respectively, and 100 % of
them had a seasonal discharge cycle (Fig. 5). Means and SDs of the
standardized Ampli were 0.59±0.46 for NO3, 0.53±0.30
for DOC, 0.79±0.14 for SRP, and 1.99±0.38 for discharge. The
distribution of the calculated seasonality indices is provided in
Fig. S7.
The annual phases for discharge were more stable among all catchments than
those for concentrations. The highest discharge period was centred on
mid-February (winter) and the lowest discharge period on September. A strong
gradient of hydrological dynamics was observed among catchments (Figs. 5d and
S7). The highest W2 was associated with both severe low-flow
discharge and many high discharge events. Values of Qmean, BFI, W2, and
QMNA clearly followed an east–west gradient (not shown). Because of similar
seasonal discharge dynamics in all catchments, SI can be used to describe
the seasonal dynamics of a concentration relative to those of discharge.
When SI was positive, the concentration seasonality was in phase with
discharge; when negative, the concentration seasonality was out of phase
with discharge (Fig. 5).
Most of the catchments had opposite dynamics for DOC and NO3. For
90 % of them, Pearson correlation between the daily GAM estimates of DOC
and NO3 was negative and for 50 % of the catchments less than
-0.79. The remaining 10 % of catchments (15) had low Ampli of DOC and
NO3. The DOC and NO3 concentrations had out-of-phase seasonal
cycles, as shown by the negative correlation between SI and DOC or NO3
for all catchments that had a significant seasonality in these
concentrations (Fig. 6; R2=0.62). We classified two types
of catchments according to their seasonality in both DOC (MinPhase) and
NO3 (MaxPhase) concentrations and consistent with the SI (Figs. 6,
S7). NO3 MaxPhase and DOC MinPhase that occurred before 1
May were classified as “in phase” with discharge (Q), while those that
occurred after were “out of phase” with Q, as proposed by Van Meter et al. (2019). All catchments experienced high stability of the DOC MaxPhase and
NO3 MinPhase were the same for all catchments as they always occurred
between July and December (Figs. 5, S7).
The first type, “in phase” (68 % of the catchments with seasonality),
had a NO3 MaxPhase between October and May (Figs. 5, S7)
(i.e. high-flow period, in phase with maximum discharge and usually with DOC
MinPhase). For these catchments, the mean SI was positive for NO3 (0.22±0.19) and usually negative or null for DOC (0.00±0.13). They
tended to be located toward central Brittany and be associated with
mesoscale catchments (mean of 52.6±38.8 km2). They
had large Ampli for NO3 and low Ampli for DOC (mean relative Ampli of
0.83±0.46, and 0.44±0.23 for DOC) and relatively low C50 of
NO3 (means of 5.74±2.46 mg N L-1 and 5.92±2.00 mg C L-1).
The second type, “out of phase” (32 % of the catchments with
seasonality), had a DOC MinPhase and NO3 MaxPhase between May and
September (Figs. 5, S7) (i.e. low-flow period, out of phase with
maximum discharge). For most catchments, maximum NO3 and minimum DOC
concentrations occurred a mean of 1.85 months before minimum discharge or
5.5 months after maximum discharge, respectively. For these catchments, the
mean SI was negative or null for NO3 (-0.08±0.06) and weakly
positive for DOC (0.21±0.10). These catchments were close to the
coast and relatively small (mean of 31.4±21.7 km2 ).
The had smaller Ampli than “in-phase” catchments for NO3, and higher
Ampli for DOC (mean relative Ampli of 0.13±0.13, and 0.74±0.30 for DOC) and relatively high C50 of NO3 (means of 8.27±2.90 mg N L-1 and 5.00±1.62 mg C L-1).
Some catchments had intermediate behaviour between these two types (Figs. 5
and 6). Some had a plateau with maximum NO3 and minimum DOC
concentrations from winter to summer, while others showed two maxima for
NO3 or two minima for DOC (one synchronous with maximum discharge and
another with minimum discharge). Other catchments also had maximum NO3
synchronous with discharge, but minimum DOC after maximum discharge.
The seasonal dynamics of SRP were more stable than those of DOC and
NO3, but less stable than those of discharge. Thus, there was only one
type of seasonality for SRP, which was out of phase with flow: MaxPhase SRP
dominated in summer (mid-August ± 1.4 months), and MinPhase SRP
dominated in late winter (March ± 1.2 months) (Figs. 5, S6),
except for two catchments with maximum SRP in January–February.
Seasonal dynamics of (a) nitrate N (N-NO3), (b) dissolved
organic carbon (DOC), (c) soluble reactive phosphorus (SRP), and (d) daily
discharge modelled by generalized additive models, for 185 headwater
catchments. To compare concentrations, they are standardized by their mean
interannual concentration. The colour gradient represents the seasonality
index of each parameter; thus, a headwater catchment's colour can vary among
panels.
Relationship between the seasonality indices (SI) of nitrate N
(N-NO3) vs. dissolved organic carbon (DOC) in the headwater catchments
for which seasonality was significant for both parameters (n=98). The
colour and shape of symbols identify the seasonality types based on the
NO3 MaxPhase and DOC MinPhase metrics. The threshold date was 1 May:
MaxPhase metrics that occurred before were classified as “in phase” with discharge
(Q), while those that occurred after were “out of phase” with Q. The DOC
MinPhase metric is shown to highlight the synchrony between minimum DOC and
maximum N-NO3 concentrations.
Controlling factors of concentration and discharge percentiles and
seasonality
The C50 of DOC was correlated significantly with 15 spatial variables and
most strongly (|rs|≥0.4) with topographic index,
QMNA, and the other hydrological indices. The C50 of NO3 was correlated
significantly with 12 spatial variables, in particular diffuse agricultural
sources (rs= 0.68 for the percentage of summer crops, rs>0.39 for N and P surplus, and rs= 0.48 for soil
erosion rate) and hydrological indices, through the base flow index (BFI)
(positively) and W2 (negatively) (Table 3). The C50 of SRP was correlated
significantly with more variables (18), but the correlations were slightly
weaker. It correlated most strongly with soil P stock (rs=-0.40),
climate and hydrology (rs=-0.43 to -0.34 with effective rainfall,
Qmean, QMNA), elevation, and hydrographic network density. It had weaker
positive correlations (rs<0.3) with the soil erosion rate
and domestic and agricultural pressures (urban percentage and P surplus).
Ampli and SI for DOC and NO3 were correlated most with the hydrodynamic
properties, followed by agricultural pressures (Fig. 7, Table 3). The
catchments “in phase” with discharge (i.e. positive SI–NO3 and
negative SI–DOC correlations) were associated with high hydrological
reactivity (low BFI and high W2) and a low percentage of summer crops (Table 3). Conversely, catchments “out of phase” with discharge (i.e. negative
SI–NO3 and positive SI–DOC correlations) were associated with low
hydrological reactivity (high BFI and QMNA, low W2) and a high percentage of
summer crops.
Correlations of SI with catchment descriptors were weaker (|rs|≤0.4) for SRP than for DOC, NO3, and discharge
because most catchments had the same seasonal pattern, with maximum SRP
concentration during low flow. Catchments with the highest amplitudes of SRP
concentration were associated with low QMNA and Qmean, high W2, low
effective rainfall, and low soil P stock. Interannual loads were correlated
mainly with hydrological descriptors (positively with Qmean and QMNA, and
negatively with W2) (Table 3). Interannual NO3 loads were also
correlated with the percentage of summer crops and soil TP content, while
interannual SRP loads were correlated weakly with the percentage of summer
crops, agricultural surplus, erosion, and point sources. Discharge
indicators present some obvious correlations (e.g. Q50 and annual amplitude
with Qmean and QMNA). Q50 and, in a lower degree, annual amplitude are
positively correlated with baseflow index (BFI) and negatively correlated
with flow flashiness (W2). This indicates that in catchments where streams
are more influenced by groundwater (generally those flowing on granite), BFI
is high and flow flashiness is low.
Correlations with catchment characteristics are lower than expected for the
Q50. Q50 is significantly correlated with wetness topographic index
(meanTWI, rs=-0.53), which indicates that Q50 is increasing in catchments
with drier soils (meanTWI low). Positive correlation with granite indicates
that discharge is more supported by this type of rocks, which present
favourable groundwater storage. Q50 is positively correlated with soil TP,
which is higher on granite substratum. Q50 is positively correlated with
SummerCrop and negatively with WinterCrop, underlying higher runoff in
catchments with non-cultivated soil during winter.
Relationship between the seasonality index (SI) of
dissolved organic carbon (DOC) and nitrate
(NO3) and the hydrological reactivity descriptors (a)
flow flashiness index (W2) and (b) base-flow index (BFI) for 124 headwater
catchments.
Spearman rank correlations between water quality indices for
dissolved organic carbon (DOC), nitrate (NO3), soluble reactive
phosphorus (SRP), discharge indices (Q), and geographical descriptors. Only
significant correlations (p≤0.05) are shown, and bold text indicates
|r|≥0.40.
DOC NO3SRP QSpatial variable C50AmpliSILoadC50AmpliSILoadC50AmpliSILoadQ50AmpliTopographyArea–-0.24––––––––––––Elevation-0.46-0.18–––-0.31-0.20.19-0.2––0.380.380.37Density_hn–––––-0.22–0.16-0.3-0.270.190.250.25–meanTWI0.54––––0.410.25-0.330.390.25–-0.53-0.53-0.59IDPR––––––––-0.21-0.19–0.20.2–GeologyGranite_pm––0.210.41–-0.43-0.310.27-0.26-0.24–0.430.430.35Schist_pm–-0.21-0.37-0.29-0.160.250.22-0.23–––-0.25-0.25–Other_pm–0.320.35–0.28–––0.280.16––––SoilErosion-0.360.24––0.480.16-0.260.390.240.17––––OC_soil-0.27-0.21–––-0.29–0.18-0.2-0.19–0.340.340.32TP_soil-0.44––0.38–-0.51-0.340.49-0.4-0.32–0.780.780.71Land useSummerCrop-0.30.280.54–0.68–-0.470.54––0.290.290.29–WinterCrop0.19–-0.2-0.29–0.480.21-0.230.17–-0.18-0.51-0.51-0.34Forest–-0.17-0.30.23-0.37-0.47––-0.29-0.19–––0.25Pasture––––-0.3–0.26-0.2––––––Urban––––––––0.23–––––N and P diffuse andN_surplus-0.210.2––0.39––0.38––0.290.280.28–point sourcesP_surplus-0.240.33–-0.220.49–-0.320.370.2-0.19–0.20.2–N_point–-0.17––––––––––––P_point–-0.16–––––0.21––––––HydrologyQmean-0.490.19–0.530.16-0.58-0.420.67-0.39-0.310.210.950.950.9QMNA-0.520.250.410.480.42-0.54-0.560.76-0.34-0.320.350.940.940.7BFI-0.41-0.270.640.380.54-0.52-0.690.57-0.2-0.230.320.720.720.21W20.43–-0.61-0.46-0.490.540.68-0.590.20.2-0.26-0.76-0.76-0.3Precipitation-0.5––0.47–-0.6-0.390.6-0.43-0.330.180.880.880.86Wetland0.16–0.310.38––––––––––DiscussionInterpretation of the spatial opposition between DOC and NO3
Spatial opposition between DOC and NO3 concentrations has been reported
for a wide range of ecosystems. Taylor and Townsend (2010) found a
non-linear negative relationship between them for soils, groundwater,
surface freshwater, and oceans, from global to local scales, and highlighted
that this negative correlation prevails in disturbed ecosystems. Goodale et
al. (2005) reported a similar negative correlation among 100 streams in the
northeastern USA. Heppell et al. (2017) found that DOC and NO3
concentrations were inversely correlated with the BFI in six reaches of the
Hampshire Avon catchment (UK). Our contribution brings an original focus on
this relationship in headwater catchments with high domestic and
agricultural pressures. Taylor and Townsend (2010) interpreted this spatial
opposition as a response of microbial processes (i.e. biomass production,
nitrification, and denitrification) to the ratio of ambient DOC : NO3,
which controls NO3 export/retention in catchments (see also Goodale et
al., 2005). In semi-natural ecosystems, high but poorly labile soil organic
C pools were associated with lower N retention capacity and thus higher N
leaching (Evans et al., 2006). Similarly, several studies (e.g. Hedin et
al., 1998; Hill et al., 2000) suggested that DOC supply limits in- and
near-stream denitrification. In contrast, other studies claimed that N can
influence loss of DOC from soils by altering substrate availability or/and
microbial processing of soil organic matter (Findlay, 2005; Pregitzer et
al., 2004). In our study, C50 was correlated with both BFI and QMNA,
positively for NO3 and negatively for DOC, which suggests that
catchments strongly sustained by groundwater flow produced higher NO3
and lower DOC concentrations, as reported in other rural catchments (e.g.
Heppell et al., 2017). The C50 of NO3 increased with agricultural
pressures (percentage of summer crop, N surplus), as observed by Lintern et
al. (2018), while that of DOC increased in flatter catchments, which is
consistent with results of Mengistu et al. (2014) and Musolff et al. (2018).
This suggests that this spatial opposition between DOC and NO3 results
from the combination of heterogeneous human inputs, heterogeneous natural
pools, and different physical and biogeochemical connections between C and N
pools. In surface water, these heterogeneous sources are expressed to
differing degrees depending on the catchment's hydrological behaviour. When
deep or slow flow paths dominate, they store and release N via groundwater
and mobilize little the sources rich in organic matter. When shallower and
faster flow paths dominate, they transport some of the N via compartments
rich in organic matter, which causes N depletion and release of more DOC to
the streams. The initial amounts of NO3 along these flow paths are a
function of human pressures.
Interpretation of the temporal opposition between DOC and NO3
The seasonal opposition between DOC and NO3 concentration dynamics
could be another manifestation of the spatial opposition between DOC and
NO3 sources, because the strength of the hydrological connection
between sources and streams varies seasonally (e.g. Mulholland and Hill,
1997; Weigand et al., 2017). The direct contribution of biogeochemical
reactions that connect DOC and NO3 cycles may also vary seasonally
(Mulholland and Hill, 1997; Plont et al., 2020). Indeed, temperature,
wetness condition, and light availability influence rates of these organic
matter reactions (Davidson et al., 2006; Hénault and Germon, 2000; Luo
and Zhou, 2006). In addition, the relative importance of the fluxes produced
or consumed via these reactions appears clearer during the low-flow period,
when the fluxes exported from the terrestrial ecosystem and delivered to the
stream decrease. These reactions consume NO3 (e.g. denitrification,
biological uptake) and release (reductive dissolution) or produce
(autotrophic production) DOC. Of the two seasonal NO3–DOC cycles, the
most common in our datasets is thus maximum NO3 in phase with maximum
discharge and minimum DOC, which has been reported in Brittany (Abbott et
al., 2018b; Dupas et al., 2018) and elsewhere (Van Meter et al., 2019; Dupas
et al., 2017; Halliday et al., 2012; Minaudo et al., 2015; Weigand et al.,
2017). The main control of seasonal DOC–NO3 cycles appears to be
related to hydrological indices (expressed as BFI and W2). Hydrological
flashiness reflects the relative importance of subsurface flow compared to
deep base flow (Heppell et al., 2017); thus, low BFI (or high W2) would
indicate higher connectivity with subsurface riparian sources and shorter
transit times. This is consistent with results of Weigand et al. (2017), who
observed higher seasonal amplitudes in DOC and NO3 concentrations and
stronger temporal anti-correlation between DOC and NO3 concentrations
in stream water dominated by subsurface runoff.
Our results are consistent with these previous results, while the
correlations with catchment characteristics can provide some explanation.
Catchments with low BFI have larger shallow flows and experience seasonal
DOC–NO3 cycles that are in phase with flow and have higher NO3
amplitudes. These cycles can be interpreted as the combination of several
mechanisms (Fig. 8):
synchronization (i.e, coincident timing) of NO3-rich and DOC-poor
groundwater contribution with maximum flow;
large contribution of near-/in-stream biogeochemical processes at reduced
low flows that decreases NO3 concentration (e.g. NO3 consumption
by aquatic microorganisms, biofilms, macrophytes, and redox processes);
large DOC-rich riparian contribution throughout the year but larger in
autumn, when flow starts to increase, as described in detail in previous
AgrHys Observatory studies (Aubert et al., 2013; Humbert et al., 2015).
In contrast, catchments with higher BFI have smaller shallow flows and
experience mainly DOC and NO3 cycles that are out of phase with flow
and have lower amplitudes. These cycles can be attributed to the following:
The groundwater contribution is more continuous, combined with a decrease in
agricultural pressures over time, and consequently a decrease of NO3
concentration in shallower/younger groundwater than in the deeper/older one
(Abbott et al., 2018b; Martin et al., 2004, 2006). This
vertical gradient in groundwater supply could explain why NO3
concentrations peaked during the annual discharge recession, which is
sustained mainly by deep groundwater inputs.
There is little contribution of near-/in-stream biogeochemical processes at reduced
low flows due to larger inputs from groundwater, which maintains a
relatively high minimum NO3 concentration.
Contributions of DOC-rich riparian sources, mainly in autumn, are
smaller than those in in-phase catchments, again due to a predominantly
deeper geometry of water circulation.
Conceptual diagram of seasonal flow paths involved in the
DOC–NO3 seasonal cycles leading to (a) in-phase cycles
with discharge or (b) out-of-phase cycles with discharge.
Interpretation of the spatial and temporal signature of SRP
The correlations between the C50 of SRP and geographic variables highlighted
the importance of P sources (soil P stocks, followed by domestic and
agricultural pressures) and surface flow paths (e.g. hydrological indices,
elevation, erosion risk). Similarly, analysis of regression models that
predicted spatial variability in total P concentration of 102 rural
catchments in Australia also indicated positive effects of human-modified
land uses, natural land uses prone to soil erosion, mean P content of soils,
and to a lesser extent, topography (Lintern et al., 2018). They always
included the percentage of urban area, which suggests a considerable effect
of sewage discharge, even at low levels of urbanization. The catchments
analysed in the present study have a homogeneous and relatively dense
distribution of small villages but no large city, which seems to support
this last hypothesis. Sobota et al. (2011) studied spatial relationships
among P inputs, land cover, and mean annual concentrations of different forms
of P in 24 catchments in California, USA. They found that P concentrations
were significantly correlated with agricultural inputs and, to a lesser
extent, agricultural land cover but not with estimates of sewage discharge.
Nonpoint sources of P in agricultural runoff, historical inputs of
fertilizer and manure in excess of crop requirements, have led to a build-up
of soil P levels, particularly in areas of intensive crop and livestock
production (Sharpley et al., 1994). This led to correlations between soil P
and runoff concentrations in agricultural catchments (Cooper et al., 2015;
Sandström et al., 2020), as found here.
The seasonality of SRP was generally the same in the region studied, and C50
and amplitudes were significantly correlated. A peak in seasonal SRP
concentrations at low flow has been reported previously (Abbott et al.,
2018b; Bowes et al., 2015; Dupas et al., 2018; Melland et al., 2012). It is
interpreted as the result of a dominance of point sources diluted during
high flow (Minaudo et al., 2015, 2019; Bowes et al., 2011) or of stream-bed
sediment sources for which P release increases with temperature (Duan et
al., 2012).
Correlation between spatial patterns of NO3 and SRP was expected given
the dominant agricultural origin of N and substantial agricultural origin of
P, but it was not observed in all catchments. The C50 of NO3 and SRP
was high mainly on the northwestern coast, perhaps due to intensive
vegetable production associated with a dominance of mineral fertilization
(Lemercier et al., 2008). Elsewhere, a high proportion of allochthonous P in
the topsoil results from livestock farming and manure application (Delmas et
al., 2015). The P-retention capacity of soils (related to their Al, Ca, Fe,
and clay contents) is also likely to increase spatial variability in the
release of P from catchments (Delmas et al., 2015). Synchronous variations
in SRP and DOC, such as those observed in small, completely agricultural
headwater catchments without villages (Cooper et al., 2015; Dupas et al.,
2015b; Gu et al., 2017), were not observed in the present set of catchments.
We assume that synchronicity of SRP and DOC in small catchments depends on
soil processes, such as reduction of soil Fe oxyhydroxides in wetland zones
(Gu et al., 2019), which are hidden by in-stream processes (P adsorption on
streambed sediments) and downstream point-source inputs (especially P
inputs) in the set of larger catchments studied.
Regarding the geographic data used as spatial descriptors, the region
studied did not have a few dense urban centres but rather smaller domestic
points scattered across the region, which is harder to characterize finely.
Moreover, Brittany's coastlines may have higher population densities in
spring and summer due to tourism. Refined estimates of domestic point
sources and their seasonal variations would be useful in future analyses.
Hydrological vs. anthropogenic controls of spatial variability in water
quality
Among the headwater catchments selected, the human pressures (agriculture
for NO3 and sewage water discharge for SRP) influenced the C50 and
loads of NO3 and SRP. However, the influence of hydrological
descriptors on the spatial variability in their loads suggested a
transport-limited behaviour of these catchments (Basu et al., 2010). Nutrient
load estimates had high uncertainties due to (i) using modelled flow data when
measurements were not available and (ii) the frequency of concentration data
(monthly), which is low for estimating nutrient loads (especially of P)
(Raymond et al., 2013). Thus, these load estimates allowed only their
relative spatial variation to be analysed. Although land-use or agricultural
pressure variables, in combination with rainfall and discharge variables,
are good predictors of nutrient loads at larger scales (Dupas et al., 2015a;
Grizzetti et al., 2005; Preston et al., 2011), the correlations with loads
were lower in the set of headwater catchments selected. For NO3, this
can be explained by higher spatial variability (CVs) in water fluxes than in
concentrations (Table 2), which can explain the dominance of hydrological
fluxes in the spatial organization of nutrient loads. Such dominance was
found to increase with the level of human pressure in Thompson et al. (2011)
for NO3. In this study, such a relationship was not visible as all the
catchments exhibited a transport-limited behaviour. It may also suggest that
the nutrient-surplus data at the local scale remained uncertain (Poisvert et
al., 2017) or that, at this scale, data on agricultural practices would be
more relevant and that variability in concentration depends less on the
magnitude of nutrient inputs than on their locations.
The catchments studied have clear seasonal dynamics in concentration, which
is consist with previous observations (Minaudo et al., 2019; Abbott et al.,
2018a). The seasonal pattern is controlled mainly by hydrological variables.
It partly reflects the mixing of contrasting sources that are connected to
streams by seasonally varying flow paths with nutrients that are transferred
vs. nutrients that are processed locally in hotspots (e.g. riparian buffer,
stream water, stream sediments) or delivered over point sources. The
seasonal NO3–DOC pattern seemed to become somewhat homogenous among
catchments larger than 100 km2, where seasonal cycles with maximum
NO3 in phase with flow seemed less common. This may be related to an
increase in in-stream biological activity during summer as catchment size
increases, enhanced by a lower stream water level and slower discharge
(Minaudo et al., 2015). Therefore, the potential relationship between
seasonal cycle type and catchment size should be studied over a wider range
of catchment sizes and nested catchments to include variations along the
hydrographic network.
Implications for headwater monitoring and management
The high regional and seasonal variations of nutrient concentrations in
streams probably drive high variations of nutrient stoichiometry along the
hydrological cycle and over the region, and, consequently, high variations
in time and space of eutrophication risks downstream (Westphal et al.,
2020). Due to the combination of anthropogenic and hydrological drivers in
explaining these stream concentrations, a better estimation of nutrient
inputs and discharge in all headwater catchments is important to predict
areas at risks, as a first step. The spatial analysis shows high and poorly
structured spatial variations of concentrations over the region.
Nevertheless, the opposition between NO3 and DOC concentrations
suggests that the C : N ratios will be even more variable:
In space, catchments with high DOC C50 and low NO3C50 will exhibit
very high C : N and vice versa.
Over the seasons, a minimum of DOC and maximum of NO3 concentrations
are in phase: catchments where DOC–NO3 variations are in phase with Q
will exhibit a low C : N ratio in winter high-flow periods and higher C : N ratio
during low-flow periods. The N : P ratio in these catchments will be high
during the low flow periods (high NO3 and low SRP concentrations).
Catchments where DOC–NO3 variations are out of phase with discharge
will exhibit probably less variation in their ratios (because of lower
NO3 amplitude) with a relatively higher winter C : N ratio than the
previous type of catchments.
We can stress that monitoring C–N and P is important as each of these
elements can follow a different pattern, even in neighbouring catchments.
Yet, these three basic elements are not always included in water quality
monitoring. Therefore, sampling programmes in which all three of those
elements are quantified should be maintained over the long term. Such
programmes will be necessary to further investigate the variations of these
element concentrations in relation with geomorphological and climate
conditions.
In this paper, we used inter-annual mean values for DOC, NO3, and SRP
loads to establish the spatial variability and seasonal patterns across
headwater catchments. Because we demonstrated that the seasonality index
(SI) and flow flashiness (W2) are linked, our results can be used to
classify non-monitored catchments as a function of their potential load
flashiness. Flow flashiness (W2) combined with SI, or the slope of C–Q
relationships for high flows, could be employed for a sampling or monitoring
design to improve annual or seasonal load estimations for the most
contributive catchments (Moatar et al., 2020). However, other issues, such as
the assessment of eutrophication risk for some lakes, estuaries, or bays
around the peninsula, would require more frequent sampling, especially for
SRP.
Conclusion
To analyse spatial variability in water quality at a regional scale, we used
an original dataset from public databases, seldom used by the scientific
community, for the French region of Brittany with monthly measurements of
water quality. The dataset selected covers 185 headwater and agricultural
catchments monitored over a period sufficiently long (10 years) to allow the
spatial (regional) variability and temporal (seasonal) variation in DOC,
NO3, and SRP concentrations to be analysed. We described
spatio-temporal variations in concentrations, loads, and seasonal patterns
and analysed their correlations with geographic variables (related to
topography, hydro-climate, geology, soils, land uses, and human pressures).
Our study showed the following:
Seasonal cycles of DOC and NO3 concentrations are usually opposite of
each other. Catchments with a low base-flow index exhibit maximum NO3
in phase with maximum flow, while those with a higher base-flow index
exhibit maximum NO3 after maximum flow. Both types exhibited maximum
DOC in autumn, at the beginning of the annual increase in flow.
NO3 concentrations increased as human pressures and base flow
contribution increased. DOC concentrations decreased as rainfall, base flow
contribution, and elevation increased. SRP concentrations showed weaker
correlations with human pressures, rainfall, and hydrological and
topographic variables.
Seasonal SRP cycles are synchronized in nearly all catchments that have a
clear seasonal amplitude, with maximum SRP concentrations that occur during
the summer low-flow period due to a decreased dilution capacity of point
sources.
The spatial and temporal opposition between DOC and NO3 concentrations
likely results from a combination of heterogeneous human inputs and
biogeochemical connection between these pools. The seasonal cycles in stream
concentrations result from the mixing of water parcels that followed
contrasting flow paths, combined with high spatial variability in nutrient
sources, local-scale biogeochemical processes, and point sources. As a
perspective, we recommend further studies of multiple elements that are
likely to show contrasting responses to diverse human pressures and to the
retention/removal capacities of hydrosystems.
Code availability
We used existing code from packages developed by other authors for the R software (Grolumund and Wickham, 2011; Le et al., 2008; Wickham, 2011; Wood, 2017; Zambrano-Bigiarini, 2020).
Data availability
We used available public data produced by the French government or public research institutes; please refer to Sect. 2.2 and Table 1 for links and information about the origin of the data and their access.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-25-2491-2021-supplement.
Author contributions
SG conducted data treatments and analyses and wrote the “Materials and methods” and “Results” sections. CGO, FM, and OF designed the study. CGO, FM, OF, GG, and SG planned the analyses and discussed the results all together. OF wrote the “Introduction” and “Discussion” sections. AC and LS helped in conducting the data analyses. CM helped in conducting the data treatments. FC provided expertise on the N and P surplus data. All authors wrote the outline of the article together and provided feedback on the article and especially on the “Discussion” section.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Remi Dupas (INRAE Rennes) for his valuable
contribution for methodological choices and the scientific interpretations
and discussions. We thank Vazken Andreassian (INRAE Anthony) for
providing regional simulations of discharge time series with the model GR4J.
We thank also Josette Launay (CRESEB), Elodie Bardon (Observatoire
Environnement Bretagne), Yves-Marie Heno, and Olivier Nauleau (DREAL
Bretagne) for their contributions to the data selection and to the project.
Finally, we thank all the people who contributed to the collection of
public data on surface water quality in French Brittany.
Financial support
This research has been supported by the Région Bretagne (grant no. 16007508) and the Agence de l'Eau Loire Bretagne (grant no. 0).
Review statement
This paper was edited by Genevieve Ali and reviewed by two anonymous referees.
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