Human activities disrupt the temporal dynamics of salinity in Spanish rivers

Abstract

Human activities are not only increasing salinization of rivers, they might also be altering the temporal dynamics of salinity. Here, we assess the effect of human activities on the temporal dynamics of electrical conductivity (EC) in 91 Spanish rivers using daily measures of EC from 2007 to 2011. We expected rivers weakly affected by human activities to have low and constant ECs, whereas rivers strongly affected by human activities should have high and variable ECs throughout the year. We collected information on land use, climate, and geology that could explain the spatiotemporal variation in EC. We identified four groups of rivers with differences in EC trends that covered a gradient of anthropogenic pressure. According to Random Forest analysis, temporal EC patterns were mainly driven by agriculture, but de-icing roads, mining, and wastewater discharges were also important to some extent. Linear regressions showed a moderate relationship between EC variability and precipitation, and a weak relationship to geology. Overall, our results show strong evidence that human activities disrupt the temporal dynamics of EC. This could have strong effects on aquatic biodiversity (e.g., aquatic organisms might not adapt to frequent and unpredictable salinity peaks) and should be incorporated into monitoring and management plans.

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Hydrobiologia
https://doi.org/10.1007/s10750-022-05063-9
EFFECTS OFCHANGES INSALINITY
Human activities disrupt thetemporal dynamics ofsalinity
inSpanish rivers
AlvaroJavierMoyanoSalcedo ·
EdurneEstévez · HumbertSalvadó ·
JoséBarquín · MiguelCañedo‑Argüelles
Received: 12 July 2022 / Revised: 7 October 2022 / Accepted: 12 October 2022
© The Author(s) 2022
to have low and constant ECs, whereas rivers strongly
affected by human activities should have high and
variable ECs throughout the year. We collected infor-
mation on land use, climate, and geology that could
explain the spatiotemporal variation in EC. We iden-
tified four groups of rivers with differences in EC
trends that covered a gradient of anthropogenic pres-
sure. According to Random Forest analysis, temporal
EC patterns were mainly driven by agriculture, but
de-icing roads, mining, and wastewater discharges
were also important to some extent. Linear regres-
sions showed a moderate relationship between EC
variability and precipitation, and a weak relationship
to geology. Overall, our results show strong evidence
that human activities disrupt the temporal dynam-
ics of EC. This could have strong effects on aquatic
Abstract Human activities are not only increasing
salinization of rivers, they might also be altering the
temporal dynamics of salinity. Here, we assess the
effect of human activities on the temporal dynamics
of electrical conductivity (EC) in 91 Spanish rivers
using daily measures of EC from 2007 to 2011. We
expected rivers weakly affected by human activities
Handling editor: Sidinei M. Thomaz
Guest editors: Erik Jeppesen, Miguel Cañedo-Argüelles,
Sally Entrekin, Judit Padisák &S.S.S. Sarma / Effects of
induced changes in salinity on inland andcoastal water
ecosystems
Supplementary Information The online version
contains supplementary material available at https:// doi.
org/ 10. 1007/ s10750- 022- 05063-9.
A.J.MoyanoSalcedo(*)
FEHM-Lab (Freshwater Ecology, Hydrology
andManagement), Departament de Biologia Evolutiva,
Ecologia i Ciències Ambientals, Facultat de Biologia,
Universitat de Barcelona, Barcelona, Spain
e-mail: alvaromoyano@ub.edu
A.J.MoyanoSalcedo
Geohazards andCivil Engineering Research
Group, Department ofCivil Engineering, Saint
Thomas Villavicencio University, C/22 No 1a,
Villavicencio500003, Colombia
E.Estévez
Department ofEcology, University ofInnsbruck,
Technikerstrasse 25, 6020Innsbruck, Austria
e-mail: Edurne.Estevez-Cano@uibk.ac.at
H.Salvadó
Department ofEvolutive Biology, Ecology
andEnvironmental Sciences, University ofBarcelona,
08028Barcelona, Spain
e-mail: hsalvado@ub.edu
J.Barquín
Environmental Hydraulics Institute ‘IH Cantabria’,
University ofCantabria, PCTCAN. C/ Isabel Torres 15,
39011Santander, Spain
e-mail: jose.barquin@unican.es
M.Cañedo-Argüelles
Institute ofEnvironmental Assessment andWater
Research (IDAEA-CSIC), Carrer de Jordi Girona, 18-26,
08034Barcelona, Spain
e-mail: miguel.canedo@idaea.csic.es
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biodiversity (e.g., aquatic organisms might not adapt
to frequent and unpredictable salinity peaks) and
should be incorporated into monitoring and manage-
ment plans.
Keywords Freshwater salinization· Temporal
dynamics· Variability· Agriculture· Precipitation·
Water quality
Introduction
Freshwater ecosystems are becoming saltier worldwide
due to human activities (i.e., freshwater salinization,
FS). Agriculture is the main driver of FS (Thorshlund
etal., 2021), but there are others such as mining or the
use of salts as de-icing agents in roads (Estevéz etal.
2019; Cañedo-Argüelles, 2020). Overall, FS is a global
water quality problem that not only harms biodiversity
and ecosystems (Cañedo-Argüelles etal. 2013; Berger
etal., 2018; Hintz & Reylea, 2019), but also poses risks
to human health (Kaushal, 2016; Cañedo-Argüelles,
2020) and can limit our use of hydric resources
(VanVliet et al., 2017; Thorshlund etal., 2021). The
vast majority of studies on FS have focused on short-
term laboratory or mesocosm experiments, or on snap-
shot field studies (Kefford etal., 2003; Horrigan etal.,
2007; Birk etal., 2020). Thus, the temporal dynamics
of FS have been largely overlooked. The few avail-
able studies addressing temporal variability in FS show
that human activities can disrupt the natural temporal
dynamics of salinity in freshwater ecosystems (Tim-
pano etal., 2018; Niedrist, 2020). We argue that each
type of human activity might have a different “temporal
signature” (i.e., a characteristic temporal behavior) due
to its intrinsic properties.
In agricultural landscapes the temporal dynamics
of salinity might depend on the cultivation period and
practices (e.g., rainfed vs. irrigated crops). For example,
Gardner & Young (1988) showed that salt accumula-
tion in the Colorado River Basin was primarily driven
by excess irrigation water from croplands, and that
irrigation explained more than a third of the basin salt
load. Also, Heimhuber etal. (2019) found that extended
dry periods increased salinity due to reduced river dis-
charge and salt accumulation in agricultural regions of
the Murray-Darling Basin (Australia). Finally, Leng
etal. (2021) found a strong correlation between nutri-
ents and salinity with the discharge of agricultural irri-
gation water into the Amu Darya and Syr Darya Riv-
ers, in Central Asia. Overall, salinity is strongly driven
by irrigation during low-flow periods in agricultural
catchments (Crosa etal., 2006; Kulmatov etal., 2020).
Therefore, peaks in conductivity are most likely to
occur during planting periods, when fertilizer addi-
tion and irrigation are maximum. During these peri-
ods, the salts that have not been used by the plants are
washed into surrounding rivers and streams (Williams,
2001; Anderson etal., 2019).In mining regions where
residues are stockpiled (i.e., mine tailings) and surface
rocks are exposed to weathering, heavy rain events
can wash the salts into surrounding surface waters
(Cañedo-Argüelles et al., 2012). This leads to sharp
salinity increases that are usually brief and not captured
by conventional water quality monitoring programmes
(Cañedo-Argüelles etal., 2017; Liu etal., 2021). At the
same time, saline effluents generated as a by-product
of resource extraction might be disposed directly to
surface waters (Cormier etal., 2013b; Vengosh etal.,
2014; Sauer etal., 2016; Yusta-García etal., 2017) and
diffuse salt pollution can generate from leaks in the
waste management infrastructure (Gorostiza & Sauri,
2019). Finally, mining can lead to the salt pollution of
groundwaters (Xinwei etal. 2009; Kaushal etal. 2018;
Bondu etal. 2021), which can enter rivers and streams
at different rates depending on complex geomorpho-
logical processes that are difficult to predict (Dahl
etal., 2007; Sun & Sun, 2013). In cold regions, salts are
often applied to roads to keep them ice-free and ensure
road safety and transportation efficiency. For example,
salt application has exponentially increased in the US
since 1940 (Jackson & Jobbagy, 2005), with around
25 million metric tons of salts applied to roads in 2019
(USGS, 2020). Also, 13.4 tons of sodium chloride are
applied annually to each kilometer of roads affected by
ice in the Alpine region of Tyrol (Niedrist etal., 2020).
Commonly, rivers and streams close to roads in cold
regions experience an increase in salinity during early
spring (when the snow is melted and flows into the sur-
rounding streams) and during periods of snow-removal
from the roads (Crowther & Hynes, 1977; Ruth, 2003;
Kaushal et al., 2005; Corsi et al., 2015; Nava etal.,
M.Cañedo-Argüelles
FEHM-Lab (Freshwater Ecology, Hydrology
andManagement), Departament de Biologia Evolutiva,
Ecologia i Ciències Ambientals, Facultat de Biologia,
Institut de Recerca de l’Aigua (IdRA), Universitat de
Barcelona, Barcelona, Spain
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2020; Dugan etal., 2020; Niedrist, 2020). Cities gener-
ate a large amount of wastewaters that contributes to the
salinization of surface waters (Venkatesan etal., 2011)
and groundwaters (Li etal., 2021). Salinity attributed to
urban areas can be determined by the quantity and type
of products used by consumers (Hoekstra, 2015), and
the climatic conditions that influence the dilution capac-
ity of rivers and streams (Tiyasha etal., 2020). Also,
the efficiency of wastewater treatment plants (WWTP)
modulates the salt load of their effluents. For example,
Levlin (2014) monitored two WWTP in Stockholm and
found no significant reduction in conductivity by the
preliminary treatment, and less than a 30% reduction
by the activated sludge process(Moyano-Salcedo etal.,
2021). Overall, the salt pollution associated with waste-
water discharges depends on the WWTP configuration
(Gonçalves etal., 2019; Salcedo etal., 2021) and might
be highest during the summer, when the dilution capac-
ity of rivers and streams is reduced (Dinçer & Kargi,
2001; Van Vliet & Zwolsman, 2008).
Understanding the temporal dynamics of FS is
important because they can affect the structure and
functioning of biological communities. For exam-
ple, Kefford etal. (2007a, b, c) found that the eggs of
some freshwater invertebrates were more sensitive to
salt pollution than their larval stages. Thus, FS might
have a greater effect for macroinvertebrates during
oviposition than during larval development or during
summer, when many species have emerged from the
water. Also, many invertebrates that feed on leaf litter
are especially sensitive to salinization (Kefford etal.,
2011). This can also have implications for ecosys-
tem functioning, since aquatic invertebrates contrib-
ute to carbon cycling through leaf litter decomposi-
tion (Canhoto etal., 2021).The aim of this study was
to analyze how human activities might disrupt the
temporal dynamics of electrical conductivity (EC, a
proxy to salinity) in Spanish rivers using long-term
data at high temporal resolution. Although previous
studies have analyzed long-term salinity trends in riv-
ers (Kaushal etal., 2005; Jiang et al., 2022), this is
the first study focusing on the temporal fluctuations
of salinity at an interannual scale and a high tempo-
ral resolution. We hypothesized that rivers under low
human pressure would have low and constant ECs,
whereas rivers strongly affected by human activi-
ties would have high and variable ECs throughout
the year. We expected that the temporal dynamics of
EC in Spanish rivers would be mainly driven by (I)
agricultural activity, leading to EC peaks during the
crops’ growing season; (II) mining, leading to high
ECs near mine tailings during heavy rainfall events;
(III) transportation in cold regions, with high ECs
during snowmelt and precipitation events in spring;
and (IV) wastewater discharge in urban areas that
would lead to maximum ECs during the summer due
to low river flows.
Materials andmethods
Study area
We studied 13 river catchments covering a wide range
of land reliefs (i.e., valleys and mountains) and geo-
logical formations (e.g., carbonated rocks the eastern
and southern regions and igneous metamorphic and
rocks in the western regions) (Morán-Tejeda et al.,
2019), and differing in size (from 900 to more than
90 000 km2) (Estévez etal., 2019). They also covered
diverse climatic conditions: the central, southern,
and eastern regions present a Mediterranean climate,
whereas the northern border is dominated by a tem-
perate oceanic climate (Rivas et al., 2011). Finally,
these heterogeneous environmental conditions result
in a gradient of hydrological conditions, with some
rivers drying during the summer (Peñas & Barquín,
2019; Estévez etal., 2019).
Electrical conductivity measurements
We used daily measures of electrical conductiv-
ity (EC) for the period 2007–2011 from 91 stations
of the Automatic Water Quality Information Sys-
tem (SAICA, 2020). Using these data, a set of 24
ecologically meaningful conductivity indices (CIs)
(Table S1 in Online Resource 1) were calculated
based on hydrological indices (Richter etal., 1996;
Peñas & Barquín, 2019). These indices were divided
into three groups regarding (1) the mean annual and
monthly conductivity, (2) the magnitude and duration
of annual conductivity extremes, and (3) the timing of
extreme conductivity events.
Environmental and human drivers
We selected relevant variables that could drive the
change in the temporal dynamics of EC (TableS2 in
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Online Resource 1), which were related to land use
(n = 8), geologic characteristics (n = 2), and anthro-
pogenic pressures (n = 4). Distance to the nearest
mine (P_DMN) and distance to the nearest icy road
(P_DIR) were computed in R (R Core Team, 2021)
according to the information available from the Span-
ish National Geographic Institute (2020). To calcu-
late P_DMN, all mines with operating permits were
located. Only the mines exploiting ferrous, non-
ferrous, precious, non-metallic (e.g., salt), industrial
rocks, and coal mines were considered. To calculate
P_DIR, areas with a minimum of 30 days of snow
were selected. Then, to determine the roads where
salt was likely added, the selected areas were inter-
sected with a road map. The intersected roads were
checked using information provided by the Spanish
General Direction of Traffic (2020). Finally, the dis-
tances of each SAICA station to mines (P_DMN) and
icy roads (P_DIR) were calculated. The rest of the
variables were computed by Estevéz etal. (2019).
Assessment of the drivers of changes in the temporal
dynamics of EC
First, principal components (PCA) and clustering
(Lemenkova, 2018) analyses were performed to group
the samples according to their CIs. The multicollin-
earity of CIs was calculated using the Variance Infla-
tion Factor (VIF) function in the R package “car,”
and the CIs with highly collinearity (VIF > 5) were
removed. Then, Random forests (RF) were performed
to assess the relative importance of the environmental
drivers for explaining the variation in EC within each
group using the function “rfsrc” in the package “ran-
domForestSRC” (Ishwaran etal., 2022). ANOVA and
Tukey’s tests were used to assess the differences in CI
between clusters. Then, generalized additive models
(GAMs) were used to assess the relationship between
EC and the different drivers selected by the RF using
the function “gam” in the package “mgcv” (Wood,
2021). The GAMs incorporated independent smooths
for each cluster and time step (i.e., each day at which
conductivity was measured) and they were built using
a default Gaussian distribution. To obtain model
diagnostics, we used the “gam.check” and “appraise”
functions in the package gratia (Pedersen etal., 2019).
We assessed the differences between GAMs (i.e.,
differences in the temporal behavior of EC between
groups) by looking at the confidence intervals. If the
difference between the confidence intervals of the
fitted smooths between two sets of data (i.e., cluster
groups in our study) was non-zero, a strong difference
was assumed (Pedersen etal., 2019). Linear regres-
sions between EC and precipitation were built. All
the statistical analyses were performed in R (R Core
Team, 2021). Finally, the nomenclature proposed by
Muff etal. (2022) was used to report the results from
statistical analyses in the language of evidence.
Results
The minimum EC was always above 100 µS/cm and
the maximum EC value was 5989 µS/cm. Overall, we
found a strong decrease in mean EC (R2 = 0.001; P
value < 0.001) at a rate of 17 µS/cm per year (Fig. S1
in Online Resource 2).
Variations in temporal dynamics of EC among
Spanish rivers
Five indices related to the annual coefficient of varia-
tion (CVA), twelve to the monthly coefficient of vari-
ation (CVM), and ten indices related to the timing of
extreme EC events (JMax and JMin) were selected to
classify the rivers because of having a VIF lower than
5 (Tables S3, S3a and S3b; Online Resource 1). The
first two axes of the PCA (Fig.1) explained 56.7% of
the variance in the different CIs, and the cluster anal-
ysis resulted in four groups of stations (SCI1, SCI2,
SCI3, and SCI4; Fig.2). The first axis of the PCA was
mainly related to the coefficient of variation of the
mean annual EC (CVA) and the coefficient of varia-
tion of the mean monthly EC (CVM from month 1 to
12). The groups were arranged along this axis as fol-
lows (from positive to negative values): SCI1, SCI4,
SCI3, and SCI2. The second axis of the PCA was
positively related to the Julian day of annual maxi-
mum EC per year (JMax) and negatively to the Julian
day of annual minimum EC per year (JMin). All the
groups contained stations with both positive and neg-
ative values of this axis, but the group SC1 showed
the widest dispersion (i.e., a highest temporal varia-
tion in EC). SCI1 included 9 stations with the high-
est mean EC and standard deviation (2500 ± 930 µS/
cm); group SCI2 included 37 stations with the lowest
mean EC and standard deviation (374 ± 185 µS/cm);
group SCI3 included 26 stations with moderate-low
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mean EC and standard deviation (850 ± 268 µS/cm);
and group SCI4 included 19 stations with moderate-
high mean EC and standard deviation (1300 ± 473 µS/
cm). Figure 3 shows the EC variations by SCIs for
the study period. In agreement with the PCA analy-
sis, SCI1 showed the highest EC variations, followed
by SCI4, SCI3, and SCI2. According to the compari-
son of GAMs, SCI1 showed strong differences (i.e.,
confidence intervals in pairwise comparisons for
GAMs smooth terms was non-zero) from the rest of
the groups in terms of temporal variations in EC (Fig.
S2 in Online Resource 2). According to the Random
Forest (R2 = 0.52), the temporal variation in EC was
mainly driven by agriculture (MN_AGR), distance
to the nearest icy road (P_DIR) and mining site
(P_DMN) in SCI1; distance to the nearest icy road
(P_DIR), area occupied by moors, heathland, scrub
and shrubs (MN_SSH), agriculture (MN_AGR), and
pasture (MN_PAS) in SCI2; mining (P_DMN) and
urban areas (MN_UHD) in SCI3; and by pasture
cultivation (MN_PAS), agriculture (MN_AGR) and,
in some cases, the area occupied by coniferous forest
(MN_CNF) in SCI4 (Fig.4).
According to ANOVA, there was strong evidence
of differences between groups (P value < 0.001 in
all cases) for several of the drivers analyzed (Fig.5).
SCI1 was the most subjected to human activities,
with the highest values of agricultural land, min-
ing, and urban areas, and the lowest values of for-
est cover and calcareous and siliceous soils. On the
opposite extreme of the anthropogenic disturbance
gradient, SCI2 and SCI3 were characterized by the
highest forest and pasture cover and the lowest urban
cover, although SCI2 showed the closest distance to
roads affected by snow, and SCI3 showed the clos-
est distance to mining sites. SCI4 presented inter-
mediate values for most drivers.Finally, geological
conditions showed a weak relation to temporal EC
variations in all SCIs and had the lowest relevance
to explain EC variations in SCI1 (the most impacted
Fig. 1 Plot representing
PCA and clustering of
Synthetized Conductivity
Indices (SCIs). The points
and arrows represent the
number of SAICA stations
by cluster and the CIs,
respectively
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by human activities) (Fig.4). Also, according to the
ANOVA test, there was no evidence (P value = 0.738)
for differences in EC between calcareous (mean
EC = 920 ± 300 µS/cm) and siliceous (mean
EC = 800 ± 279 µS/cm) catchments (Fig. S3, Online
Resource 2). We found strong positive linear relation-
ships between precipitation and EC during February
(P value = 0.012) and November (P value = 0.007)
in SCI1. In SCI2, EC was strongly associated with
precipitation in March (P value = 0.027), July (P
value < 0.001), August (P value < 0.001), October (P
value = 0.002), and December (P value < 0.001). In
SCI3, EC was strongly related to precipitation in Feb-
ruary (P value < 0.001), October (P value = 0.019),
and November (P value < 0.001). In SCI4, EC was
very strongly related to precipitation in August
(P value < 0.001), November (P value < 0.001),
and December (P value < 0.035). Finally, EC was
strongly related to heavy rainfall events in SCI3 (P
value = 0.05), and to low rainfall events (< 10 mm)
in SCI1 (P value < 0.019), SC2 (P value < 0.001),
and SCI3 (P value < 0.001). The R-squared values
of the linear models are shown in TableS4 (Online
Resource 1).
Discussion
Overall, we found strong evidence for an amplifica-
tion of the temporal variability in EC in Spanish riv-
ers due to human activities. The EC was relatively
constant along the year in rivers dominated by pas-
ture and forests, whereas it experienced frequent and
strong fluctuations in rivers subjected to high human
pressure. Also, the group of sites most affected by
anthropogenic disturbance (SCI1) showed mean EC
values above the current Spanish water quality stand-
ards set to protect aquatic ecosystems (1000 µS/cm;
Real Decreto 670, 2013) and human health (2500 µS/
cm; Real Decreto 140, 2003). This aligns with pre-
vious studies showing that water quality standards in
Europe are failing to protect aquatic biodiversity from
salinization (Schuler etal., 2019; Hintz etal., 2022a,
b). Contrary to our expectations, we found that the
grouping of sites according to the temporal variabil-
ity in EC did not respond to unique human drivers,
but to a combination of them. Therefore, we cannot
claim that each human activity has its own “temporal
signature.” This is likely related with regional differ-
ences in the human drivers of FS (e.g., different crops
Fig. 2 Geographical map
showing a spatial repre-
sentation of the obtained
cluster groups of Synthe-
tized Conductivity Indices
(SCIs). The points represent
the number of SAICA sta-
tions by cluster
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have different growing seasons) and in the natural
drivers that modulate natural salinity (e.g., hydrol-
ogy). Overall, the range of EC values reported in our
study matches those reported by previous studies in
Spanish rivers (TableS5 in Online Resource 3). We
found strong evidence that the EC trends decreased
from 2007 to 2011 for the whole set of rivers ana-
lyzed. These EC trends could be linked to technol-
ogy improvements and the increase in the number
of wastewater treatment plants (Fuentes etal., 2017;
Rufí-Salís et al., 2022; Pompa-Pernía et al., 2022).
Although a decrease in EC has also been reported
for other regions (Jiang etal., 2022), it is important
to notice that many freshwater ecosystems are getting
saltier (Kaushal etal., 2005; Dugan etal., 2017) and
this trend might be amplified by climate change (Le
etal., 2019; Olson, 2019).
Agriculture was the main variable that differenti-
ated sites with high mean EC and EC variability (SC1
and SCI4) from sites with low-moderate mean EC
and EC variability (SCI2 and SCI3). This is in align-
ment with previous studies at the global (Kaushal
etal., 2018; Thorslund etal., 2021) and the Spanish
(Estévez etal., 2019) level, which identified agricul-
ture as the main driver of FS. Our study reveals that
agriculture is not only increasing the salt concentra-
tion of rivers, but also disrupting the natural tem-
poral dynamics of salinity. Although the proximity
of icy roads was not as important as agriculture, the
ANOVA tests showed very strong evidence for dif-
ferences between groups according to this variable.
So far, road salt pollution of rivers and streams has
been almost exclusively studied in Canada and the US
(Cunillera-Montcusí etal., 2022). Our results suggest
Fig. 3 Plot representing
the mean value and vari-
ability in conductivity of
each group with Synthe-
tized Conductivity Indices
(SCIs). The point and the
irregular line represent the
mean value and standard
deviation, respectively
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that this activity is partly responsible for the increase
in EC and the alteration of EC dynamics in Span-
ish rivers, as it has been found for the Alps (Niedrist
etal., 2020). Thus, we suggest that road salt pollution
of rivers and streams deserves to be further studied
in Europe. Despite wastewater treatment plants hav-
ing a weak effect on EC variability, these also deserve
attention due to the potential interacting effect of
salinity with other chemical cocktails that com-
pose the so-called freshwater salinization syndrome
(Kaushal et al., 2018, 2019, 2021, 2022). Finally,
we found weak differences in EC between rivers
according to their geological composition. This sug-
gests that human activities are overriding the influ-
ence of geology, which is the main driver of changes
in salinity in pristine rivers and streams (Meybeck,
2003).
Temporal changes in EC were very strongly
affected by precipitation during some of the studied
months. The fact that the months that showed a strong
linear relationship between precipitation and EC were
different for each group suggests that human activities
and climatic drivers interact to modulate the temporal
dynamics of salt pollution. For instance, in the case of
Fig. 4 Random forests (RF) plot representing the relative
importance of the environmental drivers for explaining vari-
ation in conductivity within each SCIs. P_DMN: Distance to
the nearest mining. P_DIR: Distance to the nearest icy road.
P_DAR: Distance to the nearest dam upstream. V_DAR: Dis-
tance to the nearest effluent discharge upstream. MN_UHD:
Area occupied by urban areas in the draining catchment.
MN_AGR: Area occupied by agricultural land in the draining
catchment. MN_CNF: Area occupied by coniferous forest in
the draining catchment. MN_PLT: Area occupied by planta-
tions in the draining catchment. MN_SSH: Area occupied by
moors, heathland, scrub, and shrubs in the draining catchment.
MN_PAS: Area occupied by pasture in the draining catchment.
MN_calc: Area occupied by calcareous rocks in the draining
catchment. MN_slic: Area occupied by siliceous rocks in the
draining catchment
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agriculture (which was most important in SCI1), daily
EC and precipitation were strongly related during
August–November, suggesting that salts could build
up in the soil during the summer and then enter the
rivers as runoff. Concordantly, Merchant etal. (2020)
found that EC significantly increased in the Cidacos
river (included in our study) during July–November
due to crop irrigation. In SCI2, where road-de-icing
and wastewater discharge were among the most
important predictors according to RF, EC was related
to precipitation during winter, spring, and summer.
These are the months when there were roads affected
by snow, salt could be washed into the rivers due to
ice melting and river flows were low, respectively.
The potential influence of road salt application on
the EC of rivers enclosed in SCI2 aligns with a previ-
ous study (Asensio etal., 2017) that found salinized
soils 3m away from roads affected by snow during
winter in some of the rivers belonging to this group
(Aragon, Araquil, and Arga). Concordantly, in our
study, these rivers showed higher mean and standard
deviation EC (670 ± 155 µS/cm) than the rest of the
Fig. 5 Results of therel-
evant environmental vari-
ables (according to RF) and
differences between SCIs
(Standardized Values). A
MN_AGR: Area occupied
by agricultural land in the
draining catchment. B P_
DIR: Distance to the nearest
icy road. C P_DMN: Dis-
tance to the nearest mining.
D V_DAR: Distance to the
nearest effluent discharge
upstream. Limitation of
5000m. E MN_CNF:
Area occupied by conifer-
ous forest in the draining
catchment. F MN_PAS:
Area occupied by pasture in
the draining catchment. G
MN_UHD: Area occupied
by urban areas in the drain-
ing catchment. H MN_calc:
Area occupied by calcare-
ous rocks in the draining
catchment. I MN_slic: Area
occupied by siliceous rocks
in the draining catchment
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Hydrobiologia
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rivers belonging to the same group (280 ± 104 µS/
cm). In SCI3, which had the greatest impact from
mining, EC was strongly related to heavy precipita-
tion in autumn. Heavy rainfalls and flash floods are
common in Spain during autumn, especially in the
Mediterranean region (Belmonte & Beltrán, 2001;
Machado etal., 2011; Camarasa, 2016; Ribas etal.,
2020), where important mining areas exist (Spanish
National Geographic Institute, 2020). These heavy
rain events are associated with EC peaks in min-
ing areas due to the washing of salts that are stock-
piled in mine tailings (Cañedo-Argüelles etal., 2012,
2017; Ladrera etal., 2017; Gorostiza & Sauril, 2019).
Finally, it is important to take into account that the
rivers included in this study are relatively large (mean
water level = 0.76 ± 0.97 m), thereby having a high
salt dilution capacity (Turunen etal., 2020). Thus, our
results need to be taken with caution, as the magni-
tude of salt pollution and the disruption of the tem-
poral salinity dynamics in smaller rivers and streams
might be higher than those reported here. The disrup-
tion of the temporal dynamics of EC can have serious
consequences for aquatic biodiversity. For example,
we found EC peaks higher than 3500 µS/cm in SC1.
These EC values are lethal to many riverine organ-
isms according to field studies and laboratory assays
(Kefford etal., 2003; Horrigan etal., 2007; Cañedo-
Argüelles et al., 2013). However, it is not only the
magnitude of the EC peaks that matter, but also their
timing. For example, during winter, many macroin-
vertebrate species are at early development stages,
which tend to be more sensitive to salinization than
the older stages (Kefford etal., 2004, 2007a, b, c).
Also, during summer, many taxa lay their eggs, which
might not hatch at high EC (Bailey etal., 2004; Kef-
ford etal., 2007a, b, c; Lawson etal., 2021). Also,
the existence of unpredictable and frequent EC peaks
along the year could difficult the adaptation of the
species to salinization and have deleterious effects
on both biodiversity and ecosystems functioning
(Cañedo-Argüelles etal., 2014; Oliveira etal., 2021).
Conclusions
This study is the first to analyze how the combina-
tion of natural and human drivers (agriculture, min-
ing, wastewater, transportation, and urban areas)
influences the temporal dynamics of EC in Spanish
rivers. We found strong evidence for a disruption of
the temporal dynamics of EC due to human activities
during the period study (2007–2011). We obtained
four groups (SCI1, SCI2, SCI3, and SCI4) of rivers
separated according to EC variability and the timing
of extreme EC events. We found different EC pat-
terns throughout the year, with some rivers show-
ing high mean EC and EC variability (SCI1 and
SCI4) and others lower and less variable ECs (SCI2
and SCI3). The disruption of the temporal dynamics
of EC did not show a clear separation between sta-
tions according to the dominance of different human
activities. Instead, we found that EC variations were
determined by a combination of multiple environ-
mental and human drivers. Agriculture was the main
driver of FS, but de-icing roads, mining, and waste-
water discharges were also important to some extent.
Also, there was very strong evidence for relationships
between precipitation and EC that could be related
to different human activities (e.g., crop irrigation or
road salt application). Overall, our results call for
more studies analyzing the ecological implications
of increased variability of EC as a result of human
activities. According to our results, it seems advis-
able to measure EC multiple times throughout the
year and establish monitoring periodicity according
to the human pressures that are operating on rivers
and the natural seasonal EC dynamics. For example,
in agricultural watersheds dominated by agriculture,
information on the timing of pesticide and fertilizer
application, irrigation, and harvesting could be very
useful to anticipate changes in ECs in the rivers. Also,
more studies on the ecological impacts of EC fluctua-
tions are needed to implement effective management
responses that protect freshwater biodiversity from
salinization.
Acknowledgements AM was supported in part by a doctoral
grant from the Ministerio de Ciencia, Tecnología e innovación
y Colfuturo (Colombia). MC was supported by a Ramón y
Cajal contract funded by the Spanish Ministry of Science and
Innovation (RYC2020-029829-I).
Author contributions All authors contributed to the study
conception and design. Material preparation, data collection,
and analysis were performed by AJMS, EE, JB, and MC-AI.
The first draft of the manuscript was written by AJMS and
MC-AI. All authors commented on previous versions of the
manuscript. All authors read and approved the final manu-
script. MC-AI and HS contributed to funding acquisition, and
supervision.
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Hydrobiologia
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Funding Open Access funding provided thanks to the
CRUE-CSIC agreement with Springer Nature. AM was sup-
ported in part by a doctoral grant from the Ministerio de Cien-
cia, Tecnología e innovación y Colfuturo (Colombia). MC was
supported by a Ramón y Cajal contract funded by the Spanish
Ministry of Science and Innovation (RYC2020-029829-I).
Data availability All data produced from this study are pro-
vided in this manuscript (and its supplementary information
files). The information on environmental drivers of freshwater
salinization was downloaded from Sistema de Información de
Ocupación de Suelos de España (SIOSE, https:// www. siose. es/
usos- de- suelo) and Instituto Geográfico Nacional, Spain (ING,
1 https:// www. ign. es/ web/ ign/ portal). Conductivity measure-
ments were downloaded from the Water Quality Information
System (SAICA network, http:// www. mapama. gob. es). Other
datasets generated (e.g., R scripts) during the current study are
available from the corresponding author upon request.
Declarations
Conflict of interest The authors declare there is no conflict
of interest.
Ethical approval The research complies with ethical stand-
ards.
Consent for publication Not applicable.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Crea-
tive Commons licence, and indicate if changes were made. The
images or other third party material in this article are included
in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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