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\begin{document}

\title{Trends in flow intermittence for European Rivers}



\author[1]{Yves Tramblay}%
\author[2]{Agnieszka Rutkowska}%
\author[3]{Eric Sauquet}%
\author[4]{Catherine Sefton}%
\author[5]{Gregor Laaha}%
\author[6]{Marzena Osuch}%
\author[7]{Teresa Albuquerque}%
\author[8]{Maria Helena Alves}%
\author[9]{Kazimierz BANASIK}%
\author[3]{Aurelien Beaufort}%
\author[10]{Luca Brocca}%
\author[10]{Stefania Camici}%
\author[11]{Csabai Zoltán}%
\author[12]{Hammouda Dakhlaoui}%
\author[13]{ANNA MARIA DE GIROLAMO}%
\author[14]{Gerald Dörflinger}%
\author[15]{Francesc Gallart}%
\author[5]{Tobias Gauster}%
\author[16]{Lahoucine Hanich}%
\author[17]{Silvia Kohnova}%
\author[18]{Luis Mediero}%
\author[19]{Ninov Plamen}%
\author[20]{Simon Parry}%
\author[21]{Pere Quintana-Seguí}%
\author[22]{Ourania Tzoraki}%
\author[23]{Thibault Datry}%
\affil[1]{HydroSciences Montpellier}%
\affil[2]{University of Agriculture}%
\affil[3]{INRAE}%
\affil[4]{UKCEH}%
\affil[5]{University of Natural Resources and Life Sciences, BOKU Vienna,}%
\affil[6]{Institute of Geophysics PAS}%
\affil[7]{University of Évora, Portugal}%
\affil[8]{Portuguese Environment Agency}%
\affil[9]{Warsaw University of Life Sciences - SGGW}%
\affil[10]{National Research Council}%
\affil[11]{University of Pécs, Hungary }%
\affil[12]{Ecole Nationale des Ingénieurs de Tunis, University of Tunis El Manar}%
\affil[13]{Water Research Institute, National Research Council, Bari, Italy}%
\affil[14]{Cyprus Ministry of Agriculture Natural Resources and Environment Water Development Department}%
\affil[15]{CSIC}%
\affil[16]{Faculté des Sciences et Techniques Guéliz, Université Cadi Ayyad}%
\affil[17]{Slovak University of Technology}%
\affil[18]{Polytechnic University of Madrid Superior Technical School of Agricultural Engineering Food Science and Biosystems}%
\affil[19]{Bulgarian Academy of Science, Sofia, Bulgaria}%
\affil[20]{Centre for Ecology \& Hydrology}%
\affil[21]{Universitat Ramon Llull - CSIC}%
\affil[22]{University of the Aegean, Greece}%
\affil[23]{National Research Institute of Science and Technology for Environment and Agriculture}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Intermittent rivers are prevalent in many countries across Europe and in
Mediterranean countries outside Europe, but little is known about the
temporal evolution of intermittency characteristics and their
relationships with climate variability. In this study, a trend analysis
is performed on the annual and seasonal number of zero-flow days, the
maximum duration of dry spells and the mean date of the zero-flow
events, on a database of 452 rivers in European and in Mediterranean
countries outside Europe, with varying degrees of intermittence. In
addition, the relationships between flow intermittence and climate are
investigated using the Standardized Precipitation Evapotranspiration
Index (SPEI) and six climate indices describing large scale atmospheric
circulation. Results indicated a strong spatial variability of the
seasonal patterns of intermittence and the annual and seasonal number of
zero-flow days, which highlights the controls exerted by local catchment
properties. Most of the detected trends indicate an increasing number of
zero-flow days which also tend to occur earlier in the year, in
particular in Southern Europe. The SPEI is found to be strongly related
to the annual and seasonal zero-flow day occurrence in more than half of
the stations for different accumulation times between 12 and 24 months.
Conversely, there is a weak dependence of river intermittence with
large-scale circulation indices. Overall, these results suggest
increased water stress in intermittent rivers that may affect their
biota and biochemistry and also reduce available water resources.%
\end{abstract}%



\sloppy


\textbf{1. INTRODUCTION}

In streams and rivers, flow intermittence is characterized by the
cessation of flow, followed or not by complete drying of the channels
(Datry et al. 2016). The spatio-temporal patterns of flow intermittence
can be extremely variable depending on climatic, geologic or topographic
contexts (Costigan et al. 2017). While many studies have been focused on
river low-flows characterization and, in particular, the possible
long-term trends due to climate change (e.g., Marx et al., 2018), far
less work has been dedicated to intermittent rivers and ephemeral
streams. Recent studies indicate trends towards less severe climatic
droughts over North-Eastern Europe, especially in winter and spring, and
the opposite in Southern Europe where more severe droughts, are
encountered (Gudmundsson and Seneviratne, 2015, Spinoni et al., 2017,
Hertig and Tramblay, 2017). Globally, negative trends in streamflow in
Europe have been reported by Stahl et al. (2010) and Bl\selectlanguage{ngerman}öschl et al.
(2019), in Spain by Gallart and Llorens (2004), Coch and Mediero (2016),
in Italy by De Girolamo et al. (2017), Germany by Bormann and Pinter
(2017) and in Cyprus by Myronidis et al. (2018).

To our knowledge, no studies have explored the trends in flow
intermittence across Europe. Snelder et al. (2013) analyzed French
patterns in flow intermittence, using as indicators the mean annual
frequency of zero flow periods and the mean duration of zero flow
periods. Unsurprisingly, the highest values of the two characteristics
coincided with the years of severe droughts. Besides climate influences,
intermittence characteristics might be strongly influenced by processes
operating at small scales, such as groundwater-surface water
interactions and transmission losses (Beaufort et al., 2019, Costigan et
al. 2017). Similarly, in different regions of the USA, Eng et al. (2015)
classified 265 intermittent streams using as descriptors the number of
zero-flow events, the median discharge and the
10\textsuperscript{th}percentile of daily flows, and they showed strong
dependency of these metrics with temporal variations of precipitation
and evapotranspiration. More generally, the probability of flow
intermittence in rivers worldwide is likely to increase with the
projected rise of temperature in future climate scenarios (Döll \&
Schmied 2012, Osuch et al., 2016, 2018, Snelder et al. 2013, Eng et al.
2015).

Previous classifications of European rivers based on their flow regime
have usually not integrated flow intermittence, or in a relatively small
sample of basins (Gallart et al., 2010, Oueslati et al., 2015). This is
probably due to the difficulties in conceptually defining the
intermittent, ephemeral and perennial states of streams (Gustard et al.,
1992, Oueslati et al., 2015, Delso et al., 2017). For low flows and
droughts, regional classifications at the European scale (Stahl and
Demuth, 1999, Hannaford et al., 2010, Kirkby et al., 2011) or national
scale (in Spain, Coch and Mediero, 2016) have been produced using most
often the flow exceeded 90\% of the time as a threshold for low flows or
drought periods. Only a few classifications of intermittent rivers based
on zero flow indicators have been proposed, in an attempt to relate
their spatiotemporal variability with catchment characteristics or
climatic variability (Kennard et al., 2010, Snelder et al., 2013, Eng et
al., 2015, Perez-Saez et al., 2017, Tzoraki et al., 2016, De Girolamo et
al., 2014, Dörflinger, 2016, Pournasiri Poshtiri et al., 2019).
Identifying homogeneous regions and the drivers of flow intermittence,
in terms of seasonality, catchment or climatic properties, could help to
estimate intermittence characteristics and trends at the regional level
(Pournasiri Poshtiri et al., 2019). Indeed, these intermittent and
ephemeral streams are underrepresented in monitoring networks and often
ungauged in Europe (Skoulikidis et al., 2017, Costigan et al., 2017).

Besides catchment characteristics, large scale climate variability may
also exert an influence on intermittence patterns. Giuntoli et al.
(2013) evaluated the relationships between low flows and large-scale
climate variability in France, using climate indices such as the North
Atlantic Oscillation (NAO), the Atlantic Multi-decadal Oscillation (AMO)
and a weather typing approach. Their results indicated an increase of
drought severity in Southern France, and the usefulness of lagged
climate indices as predictors of summer low flows. Indeed, approaches
based on weather typing or composite analysis with climatic data could
help to evaluate the synoptic ingredients associated with dry periods
and their long-term evolution and trends (Stahl and Demuth, 1999, Beck
et al., 2015, Ionita et al., 2017). For the summer 2015 drought episode
that hit large parts of Europe, Ionita et al. (2017) observed that this
event was associated with positive anomalies in 500\selectlanguage{english} hPa geopotential
height and Mediterranean Sea surface temperatures. Since these climatic
drivers are likely to have different influences in different regions of
Europe, there is a need to perform such analysis at the regional scale.

The objectives of this study are: (i) to analyze the seasonal
characteristics of flow intermittence in Europe, (ii) to test temporal
trends in the number of zero-flow days at annual and seasonal scale and
(iii) to analyze the possible relationships between the occurrence of
zero-flows and climate indices. This study relies on an unprecedented
database of intermittent rivers across Europe, which is presented in the
next section, the methodology is presented in section three and the
results in section four.

\textbf{2. DATABASE OF INTERMITTENT RIVERS}

The database of discharge time series of ephemeral and intermittent
streams have been collected in the framework of the SMIRES COST action
(Datry et al., 2017) in the different European countries in addition to
individual contributions and stations from the GRDC
(\emph{https://www.bafg.de/GRDC}) database including countries outside
of Europe such as Morocco, Tunisia and Israel. The selected rivers are
characterized by natural or moderately influenced regime, and catchment
area smaller than 2000km2. The absence of dams or reservoirs upstream of
the station gauge has been verified from a GIS analysis using the GRanD
database (\emph{http://globaldamwatch.org/grand/}). The metadata
originating from different countries should be analyzed with care and
can be misleading since the definition of ``natural'', and the
distinction between ``little influenced'' and ``heavily'' influenced
rivers may vary strongly between different countries. Also, since this
study is focusing on zero-flow days, it is possible that zero values are
put in place of missing data; this is the reason why the data had to be
checked carefully in the absence of metadata for many rivers.

Instead of using zeros, a threshold of
10\textsuperscript{-4}m\textsuperscript{3} s\textsuperscript{-1} (0.1 L
s\textsuperscript{-1}) is considered to identify zero-discharge days to
account for measurements errors of very small discharge values. However
other thresholds, such as 5 L s\textsuperscript{-1} recommended by
Gustard et al. (1992) or Delso et al. (2017) have also been tested,
yielding similar results. In addition, the individual time series have
been checked to verify if the smallest reported non-zero flows were
below 10 L s\textsuperscript{-1}l/s. If there were no non-zero flows
below 10L/s in a series, the zero flows of that series were interpreted
as wrongly reported missing values. The analyses performed in the
present work are focusing on the annual and seasonal timescales, a
hydrological year starting April 1 through March 31, and considering
summer from April to September and winter from October to March, since
this is common practice in low-flow analysis. The definition of the
hydrological years was governed by a preliminary analysis on the
seasonality of zero-flow events in Europe (mainly in summer and autumn
but also in winter). This reduces the chance of observing a flow event
in two consecutive hydrological years. The database includes 452
stations with at least two years with five consecutive zero-flow days.
This criterion has been chosen to avoid including in the database some
missing data in place of actual river intermittence, since it is
unlikely that river flow will cease only one day in one year. Indeed, if
for an annual time series only a single day with zero-flow is recorded,
it could be missing data not properly reported in the metadata. For all
stations, all years with more than 5\% missing data have been removed.
Across most stations (452), there is a common period for analysis
between 1970 and 2010 when data are available. Two annual and seasonal
metrics of duration, - the duration of the longest no-flow event
(maximum length of zero-flow days) and the total duration of no-flow
days (sum of zero-flow days) - and the mean date of no-flow days were
studied too.

\textbf{3. METHODS}

\textbf{3.1 Clustering of stations based on seasonality measures}

Directional statistics can be used to define similarity measures from
the timing of zero flow conditions. The first step is to convert dates
into the day-in-year, which is the day of a year starting from 1 April,
into an angular value (Burn, 1997):

\(\theta_{i}=\left(\text{Julian\ Date}\right)_{i}\left(\frac{2\pi}{365}\right)\)(1)

where \selectlanguage{greek}\emph{θ\textsubscript{i}}\selectlanguage{english} is the angular value in radians for the
zero-discharge day \emph{I} In leap years, the denominator was increased
by one. All zero-discharge days can then be seen as vectors with unit
magnitude and direction given by \selectlanguage{greek}\emph{θ\textsubscript{i}}\selectlanguage{english} . Then, for a
sample of \emph{n} dates, the and coordinates of the mean date can be
determined as:

\(\overline{x}=\frac{1}{n}\sum_{i=1}^{n}{\cos\left(\theta_{i}\right)}\)(2)

\(\overline{y}=\frac{1}{n}\sum_{i=1}^{n}{\sin\left(\theta_{i}\right)}\)(3)

The mean direction (the mean date) \(\in[0,\ 2\pi)\) of zero-flow dates
for a given station can be then obtained from:

\(\selectlanguage{greek}\underline{θ}\selectlanguage{english}= \arctan^*(\frac{y}{x})\) (4)

where \(\arctan^{*}()\) is the quadrant-specific inverse of the tangent
function. The measure of the variability of the \emph{n} occurrences
around the mean date is the mean resultant length:

\(\underline{r}=\sqrt{\underline{x}^2+\underline{y}^2}\) (5)

It should be noted that \(0<\leq 1\) and that near to 1 implies
little variation and high concentration of data, and near to 0 a large
variation and wide dispersion around the mean date.

The clustering is based on the and metrics calculated for winter and
summer, using the Ward distance. The identification of the optimal
number of clusters is achieved with the help of the silhouette plot and
visual inspection of the clusters obtained.

\textbf{3.2 Trend analysis}

Trend analysis has been performed using the modified Mann-Kendall (MK)
test (Mann, 1945, Hamed and Rao, 1998) on the annual and seasonal
metrics of duration and occurrence and the mean date of occurrence . The
MK rank correlation test for two sets of observations \emph{X =
x\textsubscript{1},x\textsubscript{2}, \ldots{},x\textsubscript{n}}
and\emph{Y=y\textsubscript{1},y\textsubscript{2},\ldots{},y\textsubscript{n}}is
formulated as follows, with the S statistic calculated as:

\(S=\sum_{i<j}{a_{\text{ij}}b_{\text{ij}}}\) (6)

where

\(a_{\text{ij}}=sgn\left(x_{j}-x_{i}\right)=\left\{\par
\begin{matrix}1,\ if\ x_{i}<x_{y}\\
0,\ if\ x_{i}=x_{y}\\
-1,\ if\ x_{i}>x_{y}\\
\end{matrix}\right.\ \) (7)

and \emph{b\textsubscript{ij}} is similarly defined for the observations
in \emph{Y} . Under the null hypothesis that \emph{X} and \emph{Y} are
independent and randomly ordered, the statistic \emph{S} tends to
normality for large \emph{n} . In the current work, the modified MK test
proposed by Hamed and Rao (1998) is considered, that is robust in the
presence of autocorrelation in the time series tested by modifying the
variance of the \emph{S} statistic.

In addition, to consider the issue of false positives due to repeated
statistical tests (Wilks, 2016), the False Discovery Rate (FDR)
procedure introduced by Benjamini and Hochberg (1995) has been
implemented to identify field-significant test results. With this
method, the results are considered field significant (or regionally
significant) if at least one local p-value of the test is below the
global significance level. Only 254 of the 452 selected stations, those
with at least 10 years with more than five consecutive zero-flow days,
have been considered for this analysis to avoid testing trends on very
small sample size.

\textbf{3.3 Relationships with climate}

To estimate the relationships between dry spells and climatic drivers,
namely precipitation and evapotranspiration, the correlation between the
annual and seasonal sum of zero flow days and the maximum length of
zero-flow days with the Standardized Precipitation-Evapotranspiration
Index (SPEI, Vicente-Serrano et al., 2010) was analyzed with the
Spearman correlation coefficient (rho). The SPEI uses the monthly
difference between precipitation and potential evapotranspiration; thus
it represents a simple climatic water balance, which can be calculated
at different time scales similarly to the Standardized Precipitation
Index (SPI, McKee et al., 1993). The SPEI with 6, 12, 18 and 24 months
aggregation time have been downloaded from the CSIC Global SPEI database
( \emph{https://spei.csic.es/database.html} ). The SPEI values were
computed using monthly sum of precipitation and potential
evapotranspiration at 0.5 degrees spatial resolution and a monthly time
resolution from the Climatic Research Unit (CRU) of the University of
East Anglia. Version 3.23 of the CRU dataset has been used to compute
the SPEI. The SPEI computation is based on the FAO-56 Penman-Monteith
estimation of potential evapotranspiration. The details of the SPEI
computation were presented by Beguer\selectlanguage{ngerman}ía et al. (2014). For each station,
the value was extracted from the SPEI grid cell covering the station,
since the size of the basins considered is small (\textless{}2000km²)
compared to the CRU mesh (approximately 2500km²) used as a basis for the
calculation of the SPEI.

In addition, different climate indices describing large scale
atmospheric circulation patterns have been selected: the North Atlantic
Oscillation (NAO), the Atlantic Multi-decadal Oscillation (AMO), the
Mediterranean Index (MOI), the East Atlantic Western Russia (EAWR), the
Pacific Decadal Oscillation (PDO), and the Scandinavian Index (SCAND).
The time series for these indices have been retrieved from the CPC
database available online at:\emph{https://www.cpc.ncep.noaa.gov}. The
annual, winter and summer mean values were used. For NAO, the seasonal
three-month DJF, MAM, JJA, and SON values were also included to account
for its within-year variability.

\textbf{4. RESULTS}

\textbf{4.1 Spatiotemporal patterns and seasonality}

Figure 1 shows that for a large number of stations, there are only a
small number of years with more than 10\% zero-flow days. Only a few
stations have zero-flow 90\% of the years with available data. As shown
on the map (Figure 1), the annual percentage of years with zero-flow
days can vary strongly even for neighboring stations. This highlights
the influence of local characteristics (geology, land cover, water
use\ldots{}) on zero flow occurrences. Snelder et al. (2013) previously
observed over France that the high spatial heterogeneity in small-scale
processes associated with intermittence partly explains the low spatial
synchronization of zero-flows. There is a weak latitudinal gradient in
the occurrence of zero-flow days, with the higher mean annual number of
zero-flow days in the South (rho= -0.36 with latitude, significant at
the 5\% level), but with a very strong spatial variability even for
neighboring catchments. This implies that most intermittent streams are
not necessarily associated with the aridest climate conditions in
Southern Europe.

Clustering has been applied using the variables \selectlanguage{greek}\emph{θ} , \selectlanguage{english}the mean
direction of zero-flow and the variability around this date, \emph{r} ,
computed for the winter and summer seasons. As shown in Figure 2, three
different seasonality patterns were identified. The largest one, Cluster
2, is composed of 376 stations having a mean date of occurrence for
zero-flow days between May and November, at the end of summer. The
location of the stations composing this cluster are scattered all across
Europe, in different climatic zones ranging from Continental to
Mediterranean climate types. The second-largest cluster, Cluster 3,
contains 47 stations with a mean occurrence of no-flow events between
January and March. It includes stations with a nival regime, such as the
Pyrenees of Scandinavia, that experience cessation of flow due to
freezing. The Cluster 1 (29 stations) mean corresponds to late fall
(November to January). As shown in this analysis, for most stations
(Clusters 1 and 3), the zero-flow conditions are more frequently
observed in summer months or during winter or early spring due to snow
and ice cover. Yet, as shown in the map in Figure 2, there are no clear
spatial patterns that could be identified from this analysis though
Cluster 3 stations are located predominantly in mountainous or northern
areas.

\textbf{4.2 Trend analysis}

The trend detection and subsequent analyses have been performed only for
the 254 rivers with at least 10 years with a minimum of 5 consecutive
zero-flow days. First trend analysis has been carried out on annual,
winter and summer mean dates of zero-flow occurrence, with the metric
(eq. 4) computed on an annual or seasonal basis. The results in terms of
significant trends are shown in Figure 3, indicating for most rivers in
southern Europe a trend towards earlier occurrence in zero-flow days,
mostly for the annual and summer timescales. For the river in the Baltic
region, the trend toward later occurrence in zero-flow days for the
summer and annual periods can be observed. Conversely, more contrasted
trends patterns are detected for winter, with both positive and negative
trends in the mean date for stations, but close to each other. A
significant increasing trend is visible in southern France and central
Spain for the winter period.

The second trend analysis is concerning annual and seasonal sums and
maximum lengths of zero-flow days, the results are presented in Table 1
and figure 4. At the annual scale, 60 stations (24\%) have positive
trends in the number of zero flow days and more generally there are more
positive trends detected by comparison to the negative trends for all
indicators. Since on average the trends affect about 34\% of stations,
these trends (both positive and negative) are all field-significant
according to the FDR procedure. These results indicate that at the
annual and seasonal timescales the majority of trends are towards an
increase in dryness. At the seasonal timescale, there is a marked trend
towards an increase in summer zero-flows, but fewer trends detected for
winter. Overall, fewer trends are detected for extreme durations
compared to annual or seasonal totals, with the notable exception of the
winter maximum lengths of zero-flow days that are increasing in the
majority of stations.

\textbf{4.3 Links with SPEI anomalies}

In this section, the findings of the relationships between the
occurrences of zero-flow days and SPEI anomalies are presented. In the
first step, a trend analysis has been performed on SPEI over the
different basins for different aggregation periods with 6, 12, 18 and 24
months. The results show a clear pattern with positive trends at
stations located north of 45\selectlanguage{ngerman}°N and negative trends in the south, such
findings are fully consistent with Spinoni et al. (2017). To check if
the SPEI anomalies could be explanatory covariates for the inter-annual
variability of zero-flow day occurrences, the SPEI with different
aggregation time has been correlated with the number of zero-flow days
and the maximum dry spell lengths. Since it has been shown previously
that these variables and SPEI may exhibit trends, the time series have
been de-trended in the cases where significant trends were found.
Results show a strong association of zero-flow days with SPEI anomalies
in particular at the annual and summer timescales (Figure 5), with a
lower number of significant correlations during winter. The annual or
seasonal sums of zero-flow days are more strongly associated with SPEI
anomalies than the maximum length of dry spells, a metric that is
probably strongly affected by the hydrogeology and the memory of the
basins. Figure 6 shows the map of correlation coefficients obtained with
the SPEI18. The correlations are negative for all SPEI timescales,
indicating that negative SPEI anomalies, i.e. pronounced precipitation
deficits, are linked with a larger number of zero-flow days. For about
one-third of stations (36\%) there is no significant correlation these
stations are mostly located in Spain, southern France, North Africa,
Cyprus, but also Belgium. A strong spatial variability of the pattern is
once again evident basins indicating local influences. The strength of
the correlations is higher for basins with a larger annual average
number of zero-flow days for almost all indicators. This demonstrates
that strongly intermittent basins are more influenced by climatic
variations to determine the annual number of zero-flow days. This is
probably due to the fact that these catchments are often close to the
wet/dry threshold and consequently immediately impacted by a variation
of precipitation availability. There is also statistically significant,
but moderate (rho=0.2), the correlation between the strength of the
correlations with the SPEI12 and basin size, indicating that the
influence of SPEI12 might be stronger for smaller basins since larger
basins can be more influenced by human activities. Yet, this
relationship is not significant for the SPEI18 and SPEI24. Smaller
basins have a reduced buffer capability in the context of climate
variability so this behaviour is expected.

\textbf{4.3 Relations with large-scale atmospheric circulation}

As the large scale climate drivers have high- and/or low-frequency time
variability, the correlation analysis should consider long time series.
That is why only rivers with at least 30 years of data during 1970-2010
have been examined. AMO and NAO are the most influential. At the annual
scale, the AMO was the driver with positive correlations for a
considerably high percentage of catchments, 21\% for the longest annual
no-flow event and 23\% for the annual sum. Results for both metrics in
seasons are presented in Table 2. The summer and the winter AMO are the
drivers with the strongest links to the summer and winter metrics. The
cluster of rivers positively associated with the winter AMO is well
recognizable in northern Europe (Norway, Finland) for both winter
metrics. The belt comprising Great Britain and south Sweden with a
negative link between the winter AMO and the winter sum is noticeable. A
similar pattern is observed for the summer and the annual AMO (Figure
7). Additionally, the southern, Mediterranean part of Europe might be
positively influenced by the AMO. The conclusion can be drawn that the
AMO is a potential driver of the intermittence and that its influence
can be stretched to the following season. The associations with the NAO
are less frequent. However, the apparent cluster of rivers with a
negative link to the NAO DFJ can be observed in the Scandinavian
Peninsula for both winter metrics (Figure 7). The rivers positively
linked to JJA NAO are scattered over Europe. It is worth emphasizing
that this large scale climate effect can be hidden by more local climate
conditions.

The relationship between climate drivers and hydrological
characteristics in Europe has been documented by many researchers (e.g.,
Valty et al. 2015, Wrzesi\selectlanguage{polish}ń\selectlanguage{english}ski \& Paluszkiewicz 2011). Results of the
analysis are to a large extent consistent with results obtained by
Giuntoli et al. (2013) where positive links between the annual discharge
volume deficit and the annual NAO and AMO were revealed in many rivers
in France, providing insights into the increase of drought severity.
Results are also consistent with Linderholm et al. (2009) who
reconstructed the summer NAO since the 19\textsuperscript{th} century
and revealed strong links to droughts in the Eastern Atlantic region
from a multi-century perspective. Results are similar to Hurrell and
Folland (2002) who showed that the NAO, apart from its worldwide
importance in shaping the winter hydrological conditions, can affect the
summer hydrological conditions and might force droughts.

\textbf{5. DISCUSSION AND CONCLUSIONS}

This study provides the first European-scale assessment of the trends in
intermittence over the last decades. The most striking results are (i)
the strong spatial variability of the detected trends, and (ii) the
seasonality and relationships with climate drivers. Overall, there is a
considerable spatial variability of flow intermittence, with possibly
strong dependencies of the different indicators with basin
characteristics. Most of detected trends are towards an increasing
number of zero-flow days, tending to occur earlier in the season, in
particular around the Mediterranean basin. For most basins, there is a
strong association of zero-flow days with SPEI negative anomalies, but
neighboring basins may exhibit different relationships showing again the
strong spatial variability. This implies that regional predictions or
generalizations for flow intermittence patterns should be interpreted
with caution. Any mapping or extrapolation from such regional results
may be prone to considerable errors if not considering basin
characteristics that are likely to play a strong role in determining
flow intermittence properties. In this study, the individual catchment
characteristics (ie. topography, geology, land use, soil types) have not
been analyzed, which would require a major work at such a
continental-wide scale. In particular, it would be particularly
interesting to distinguish basins with strong surface-groundwater
interactions that could explain some of the patterns described in the
present study. However, as noted by Snelder et al. (2013), flow
intermittence is also controlled by processes operating at scales
smaller than catchments, thus capturing these processes would require a
much more detailed investigation than classical regional approaches to
take into account the local physiographic characteristics (Tramblay et
al., 2010).

A major uncertainty of the present work and, to a greater extent,
applicable to all ecohydrological research on intermittent rivers is the
definition of the zero-flow days and the lack of regional
representativeness of the monitoring networks. With regard to the first
aspect, there is a wide variety of measurements procedures in different
European countries, leading to different accuracy of the measured
discharge values, in particular the minimal values. For example, in the
UK or in France the data is provided in m\textsuperscript{3} s-1 with an
accuracy of three decimals, but for many other countries, the minimum
reported discharge is sometimes much greater than 1 L s-1 due to
different measuring methods . In addition, many rating curves at open
channel stations have uncertainty at low flows caused by instability of
the riverbed. In the present work, we considered a strict criterion to
identify zero-flow days, but a more adequate selection could be made
possible if good quality metadata information were available for most
rivers, which is not currently the case for several countries. With
regard to the second aspect of regional representativeness, several
studies have highlighted the lack of measurements for intermittent
rivers (Skoulikidis et al., 2017, Costigan et al., 2017). The number of
monitored head water streams which are likely to be intermittent is
indeed much smaller than the number of perennial and large streams in
national and international databases, although their contribution to the
water resources is probably high. This questions the rationale behind
national measurement strategies for intermittent streams, in particular,
the most ephemeral ones, since they present a possibly reduced interest
in water resources management by comparison to perennial streams.
Depending on the national monitoring strategies of the river network, it
is possible that the selection of intermittent rivers to be monitored
may be biased towards a specific type of rivers within a given
geographic location of geological properties.

Another important aspect to take into consideration is the regulation
status of the monitored rivers, which may evolve over time and not be
available in the stations' metadata. In the present work, the selected
basins are those described as natural or weakly altered based on their
metadata information. Yet, how to quantitatively define ``weakly
altered'' between different national networks and monitoring protocols?
This definition may differ from one country to another without a common
objective criterion to define the percentage of natural discharge being
diverted or used for water supply. Besides river alterations, often
assumed to be an expert judgement, the quantitative evaluation of the
water uptake would require extensive work to monitor and collect water
consumption data over time. Even for a river considered to be unaltered,
diffuse groundwater pumping may occur and therefore have impacts on the
groundwater-surface interactions, which could in turn strongly impact
intermittence occurrence. Two examples of the influence of river status
as described in the metadata on the trends results may be found in the
UK. The Coal Burn River is classified as broadly natural, but is an
experimental catchment set up to assess the impact of afforestation and
the trend analysis indicates an increase in zero-flow days, showing that
the influence of land-use change might indeed be significant on this
aspect of the flow regime. The limestone Slea River that conversely
experienced a decrease in zero-flow days was also classified as natural,
but further scrutiny revealed a discharge augmentation scheme installed
in 1995. Taking into account all these local specificities in addition
to the available metadata would require tremendous effort and the
information may not always be as readily available. The main findings of
the present study are an incentive to implement process-based studies on
the intermittency characteristics for different climatic and
physiographic environments.

\textbf{Acknowledgements}

This research is part of the COST action CA15113 SMIRES, Science and
Management of Intermittent Rivers and Ephemeral Streams
https://www.smires.eu/, a short term scientific mission has been funded
for A. Rutkowska. A. Rutkowska was also supported by the Ministry of
Science and Higher Education of the Republic of Poland (grant DS 3371).
The different data providers are gratefully acknowledged. Some of the
station data was retrieved from the Global Runoff Data Centre (56068
Koblenz, Germany).

\textbf{Data Availability Statement}

The different indexes computed in the present work are made available to
the community for research applications upon request to the first
author.

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\textbf{TABLES}

Table 1: Summary of the detected trends in the annual and seasonal
number of zero flow days and the maximum length of dry spells\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Variable & Positive trends & Negative trends\tabularnewline
\midrule
\endhead
Annual sum & 23,62\% & 10,24\%\tabularnewline
Annual maximum & 20,08\% & 8,66\%\tabularnewline
Winter sum & 10,63\% & 8,66\%\tabularnewline
Winter maximum & 23,23\% & 9,45\%\tabularnewline
Summer sum & 23,62\% & 6,69\%\tabularnewline
Summer maximum & 18,11\% & 9,45\%\tabularnewline
\bottomrule
\end{longtable}

Table 2: Percentage of catchments with significant correlations
(\(\alpha=0.05\)) between seasonal metrics of intermittence and
large-scale climate drivers. The subscripts \(s,\ w,\) DJF,
JJA,\(\ w1\)refer to the summer (Apr-Sep), winter (Sep-Mar),
Dec-Feb, Jun-Aug, and winter from the preceding year, respectively.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
a) & Positive correlations & Positive correlations & Negative
correlations & b) & Positive correlations & Positive correlations &
Positive correlations\tabularnewline
variable & AMO\textsubscript{s} & AMO\textsubscript{w} &
NAO\textsubscript{DJF} & variable & NAO\textsubscript{JJA} &
AMO\textsubscript{s} & AMO\textsubscript{w1}\tabularnewline
winter max & 18\% & 16\% & 11\% & summer max & 11\% & 21\% &
18\%\tabularnewline
winter sum & 21\% & 19\% & 11\% & summer sum & 9\% & 22\% &
21\%\tabularnewline
\bottomrule
\end{longtable}

\textbf{FIGURES CAPTIONS}

Figure 1: Mean annual percentage of zero-flow days at the annual, summer
and winter timescales

Figure 2: Results of the cluster analysis on zero-flow seasonality.
Colours represent seasonality of zero-flow events. Blue colour denotes
stations with zero-flow events observed from November to January. Red
colour indicates zero-flow events in summer while green colour in the
period between January and March

Figure 3: Significant increasing (later date) or decreasing (earlier
date) trends in the mean date of zero-flow day occurrence, at the 10\%
significance level

Figure 4: Significant increasing (red triangle up) or decreasing (blue
triangle down) trends, at the 10\% significance level, for the annual or
seasonal mean number of zero flow days (left), the annual or seasonal
maximum length of dry spells (right)

Figure 5: Relative number of significant correlations at the 5\% level
between annual, summer and winter sum of zero flow days, and maximum
length of dry periods with SPEI6, SPEI12, SPEI18 and SPEI24

Figure 6: Map of the significant correlations between the annual sum of
zero flow days (top) and annual maximum length of zero flow days
(bottom) with the SPEI18. The black crosses indicate stations where the
correlation is not significant at the 10\% level. Correlations are
negative because the smaller the SPEI (water deficit), the larger the
number of zero-flow days.

Figure 7: Correlations between the summer sum of zero-flow days and
summer AMO (left), and the correlation between the winter sum of
zero-flow days with winter NAO (right)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig1-zeroflowdays/fig1-zeroflowdays}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig2-season/fig2-season}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig3-trenddates/fig3-trenddates}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig4-trends/fig4-trends}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig5-spei/fig5-spei}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig6-mapspei18/fig6-mapspei18}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig7-naoamo/fig7-naoamo}

\end{center}
\end{figure}

\selectlanguage{english}
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