4.1. Methodological considerations
As hypothesized, we observed high correlations among the 315 calculated
indices. This was particularly evident between metrics that describe
duration and frequency and those related to magnitude (Fig.2). This
result is not surprising considering the high redundancy and
multicollinearity among hydrological variables . As a consequence,
statistical techniques for selecting a reduced set of non-redundant
indices are necessary to represent the hydrological gradients . In our
study, we first selected 171 indices with the highest loading
coefficient associated with the first dimension of the PCA (Table C1),
and many of them were also correlated (Figs.AC1-AC7). Similar processes
to reduce the number of indices have been reported in previous studies
for hydrological classifications (e.g., . Considering that PCA analyses
provided many indexes for each attribute, we refined the selection using
cluster analysis and repetition of metrics in the first 5 dimensions of
PCA. However, these results need to be interpreted with caution, since
we prioritized the selection of annual indices, such as number of days
or percentage of months without flow per year, mean daily of annual
flows, coefficient of variation of Julian date of the annual start of
zero flow and annual rise rate.
As also hypothesized, our results indicate that the PCA and the
correlations between the indices are analogous for all thresholds 0, 1,
2, and 5 l/s to define zero-flow conditions. Similar results were
obtained by , who found no significant change in zero-flow indices for
thresholds of 1, 2 and 5 l/s. However, they found that upper thresholds
(i.e. 10 l/s) were not suitable for characterizing periods of low flow.
They applied different thresholds considering that river gauge stations
may have different measuring tools and some thresholds may not be
ecologically relevant. In fact, described the causes of zero-flow stream
gauge measures and their consequences (e.g., ecological status,
statistical classification, or hydrological modelling) for understanding
hydrology, biochemical, and ecological processes in NPRS.
We found that number of days per year without flow and annual percentage
of months without flow (identified in the first dimension of the PCA)
contain the greatest degree of variation for all the thresholds used to
define zero flow conditions (Table 1). These metrics, related to
duration and frequency attributes, express stream drying events and are
common to describe the flow temporality . They indicate the
unavailability of water during the dry phase and act as an environmental
bottleneck for species of these ecosystems . Furthermore, indices of
magnitude were found mainly in the second dimension of the PCA. Mean
daily annual flows is repeated in all the zero-flow thresholds and has
been reported as a major variable for hydrological characterization in
perennial rivers . Surprisingly, we found that timing and rate of change
attributes were identified in subsequent PCA dimensions; CV of Julian
date of the annual start of zero flow in the third dimension and annual
rise rate only in the fifth, both described by . Taken together, these
results suggest that duration, frequency and magnitude indices (mostly
selected in the first and second components) are more important than
timing and rate of change indices (selected from the third component).
These findings indicate that these attributes were best at predicting
the temporal variability on the flows; duration and frequency in the dry
phase and magnitude in the wet, reinforcing the importance of these two
phases for the structure and composition of the aquatic fauna . However,
in this study we have retained at least one metric for each of the five
attributes for a better characterization of the variability of the NPRS,
both in dry and wet periods, and to facilitate comparison with other
available classifications.