2.1. Study area
The study area includes 69 rivers from 15 basins located in southern
France, Spain, and Portugal covering most of the western part of the
European Mediterranean region (Fig. 1). Briefly, rivers of France are
located on 4 temporary tributaries of the Rhone-Mediterranean basin
district, very close to the Mediterranean Sea. In Spain, we studied 44
rivers, of which 16 are located in river basin districts that flow into
the Mediterranean Sea (Catalonian, Ebro, Júcar, Segura and Andalusian
Mediterranean basins) and 28 into the Atlantic Ocean (Tagus, Guadiana,
Guadalquivir, Guadalete and Barbate basins). The 21 stations of Portugal
drain into the Atlantic Ocean and belong to Guadiana, Algarve, Sado,
Mira, Vouga, Mondego, Lis, Douro, Cávado, Ave and Leça river basin
districts.
In natural conditions, all the rivers in these basins present a
Mediterranean flow regime pattern, which implies two alternate periods:
a high-flow period during the wet season (i.e. autumn to winter) and a
low-flow period during the dry season (i.e. late spring and summer).
However, the basins under study cover a large geographical area
representing a wide gradient of climatic, topographic and geologic
conditions which imply notable differences in hydrological regimes. The
climate is mostly temperate although there are stations located in
semi-arid regions of southern Spain. According to the Köppen-Geiger
classification, the studied NPRS are matched with hot (Csa), warm (Csb),
or cool (Csc) summer Mediterranean climate, and hot (BSh) or cold (BSk)
semi-arid climate . Although the climate has a common pattern with mild
and wet winters, and dry, hot, or cold summers, there are differences in
the range of precipitation and temperature. The land use in the studied
basins is dominated by agriculture, originated by the use of human
society on the natural environment . The rivers and its stream
tributaries are heavily regulated by the construction of dams and weirs,
which have substantially altered the natural flow regime also reducing
the number of unaltered gauging stations with flow data records .
(Here Fig. 1)
2.2. Hydrologic data
We used daily flow records from gauging stations in NPRS minimally
impacted by human activities. Due to the lack unaltered stations with
adequate data in Mediterranean NPRS , we assumed that most of them
contained missing data (Table A1). The selection of NPRS in almost
natural conditions implied avoiding hydrological alteration, deviation,
or cessation of water due to transverse barriers (large dams or smaller
weirs) located upstream. Data records were obtained from different
sources. In Spain, they were obtained from the national database of
public gauging stations of CEDEX (Centre for Hydrographic Studies;
https://ceh.cedex.es/), or from the corresponding River Basin Authority.
For this purpose, we first identified gauging stations without altered
flow conditions using the national inventory of barriers on
non-perennial rivers (available at https://sig.mapama.gob.es/geoportal).
In France and Portugal, stations were obtained from SMIRES project
database (https://www.smires.eu/). To identify those gauging stations in
NPRS near to natural conditions, we used the AMBER barrier Atlas
(https://amber.international/european) and hydrological pressures
collected in the European WISE database
(www.eea.europa.eu/data-and-maps/data/wise-wfd-4).
The Supporting Information of Table A1 expands the information about the
stations such as river basin where are located, country, coordinates,
gauging station code, the length of the data period, the number of days
of the data series and the number of days with missing data.
Gauging stations with more than 15 years of daily flow records were
used, except one with 11 years in the Tagus basin in Portugal (Table
A1). The median length of the data period was 36 years (IQR=26-43
years). All the series were validated for the calculation of zero-flow
hydrological indices with smires package
(https://github.com/mundl/smires). For each station we calculated a set
of 315 hydrological indices that have been previously referred in other
studies focused on perennial rivers (Eng et al., 2017; Olden and Poff,
2003), drought events in NPRS (Costigan et al., 2017; Delso et al.,
2017) and low river flows (Henriksen et al., 2006; Kennard et al.,
2010). Following Richter et al. (1996), the indices were classified into
five groups characterizing hydrological conditions related to: (i)
magnitude, (ii) frequency, (iii) duration, (iv) timing, and (v) rate of
change of flow or drought events. A list of the calculated indices and
their main characteristics is shown in Appendix B.
Finally, the hydrological indices were encoded for calculation with theR programming language . We used the lfstat package to
calculate the number and duration of zero flow events. Thehydrostats package was used for calculating the Colwell’s index
of predictability and seasonality , and the rate of change in the
magnitude of the flow and the asymmetry (skewness) of the hydrological
series. In order to calculate the indices, three conditions were
adopted. First, all years of the series have been used, even those with
incomplete records. Second, the hydrological year was set at the
beginning of the Julian calendar year. Third, we defined different
thresholds to define the days without daily flow at 0, 1, 2, and 5 l/s.
This is due to false positives of null flows associated with the
restrictions and uncertainties of the measurement of days without flow
in gauging stations of NPRS .
2.3. Hydrologic classification for non-perennial Mediterranean
rivers and streams
We used principal component analysis (PCA), an unsupervised learning
statistical technique, to examine the relationships between the
hydrological indices. Given the strong correlation between hydrological
indices , we also utilized PCA to reduce the dimensionality by selecting
prominent metrics for each attribute . Correlation matrices were used to
equalize the contribution of the indices to the PCA regardless of the
scale . Following the PCA analysis, we selected indices with the highest
loading coefficient (in absolute terms) associated to the five first
components that accounted for approximately 70% of the total inertia
for each of the zero-flow thresholds . Specifically, we reduce by more
than half the hydrological indices with the loading coefficient, but it
was not enough to choose the indices of each attribute that best define
the hydrological pattern of NPRS and respond to the diversity of
Mediterranean temporary flows. Thus, we decided to select one index for
each attribute (magnitude, frequency, duration, timing, and rate of
change) based on expert criteria and supported by statistical analysis.
Here, we used both the repetition of the indices at each threshold with
the highest loading coefficient associated (in absolute terms) to the
first five PCA dimensions and hierarchical clusters based on the
correlation distance for each group of selected indices with PCA of each
attribute (Appendix C). PCAs and clusters were executed with theFactoMineR package .
Self-Organizing Maps (SOM) were used to classify temporal rivers into
hydrological types according to similarities with the selected indices.
SOM is an unsupervised machine learning technique that uses an
artificial neural network to reduce multidimensional data into
two-dimensional nodes heatmap. This is an interactive process that
assigns a weight to each node on the map where the minimum similarity
distance is chosen and the neighbourhood of the nodes is established. We
followed the rule proposed by to determine the optimal dimension of the
number of nodes in the map. The nodes must be close to 5√n where n is
the number of samples analysed (in this study n=345). Consequently, our
map should have approximately 93 nodes distributed by a layer of 9 rows
x 10 columns. Additionally, we also evaluated the quality of the maps by
means of quantization and topographic error of different layers (from 2
x 2 to 10 x 10). To identify clusters on the SOM output map and draw the
boundaries, we applied a hierarchical cluster. The optimal number of
clusters for a SOM output was determined using NbClust package in
R , whereas SOM analysis and its graphical representation were generated
with the Kohonen package .
2.4. Comparing methods for Mediterranean non-perennial rivers and
streams classification
Finally, we compared the relationship of our results with three other
hydrological classifications developed for Mediterranean rivers.
Firstly, we used the classification of the Ministerial Order for
Hydrological Planning of the Spanish legislation that classifies rivers
into four categories depending on the number of days with presence of
water throughout the year: (i) permanent river courses if water flows
every day of the year, (ii) temporal river courses with presence of
water during an average period of 300 days per year, (iii) intermittent
with water flowing between 100 and 300 days per year, and (iv) ephemeral
flowing less than 100 days per year. Secondly, we classified rivers
following the Italian legislation depending on the number of months with
presence of water in: (i) temporal river courses without presence of
water for at least 2 of the last 5 years, (ii) intermittent with more
than 8 months with water, (iii) ephemeral with less than 8 months with
water, and (iv) episodic with water only after heavy precipitation
events. Last, we applied the classification developed by LIFE+ TRivers
project to evaluate the hydrological flow regime in NPRS according to
the aquatic phase on biological communities . This classification is
provided by TREHS free software tool and comprises four aquatic regime
types or hydrotypes classification: permanent or perennial,
intermittent-pools, intermittent-dry, and episodic or ephemeral. To
carry out the comparison between the different classifications, we
conducted an alluvial plot with ggalluvial and circlizepackage.