Analysis
We removed any days with missing data, which generally occurred because
of equipment malfunctions, such as dead batteries or water damage. When
possible, we used the redundancy of the stream sensors or camera data to
fill in missing data gaps. Camera photos were used to measure stream
flow depth to 0.05 m increments on an hourly time scale. We summarized
each measurement on the daily scale, for example mean daily stage
height, as an input in our calculations.
We selected five ecologically relevant and informative flow metrics
encapsulating all five major components of a flow regime (Poff et al.
1997): (1) skew in daily stage or the mean daily stage divided by the
median daily stage (magnitude), (2) Julian date of first no-flow day
(timing), (3) number of no-flow days (duration), (4) high pulse count,
where a pulse is defined as a flow event greater than 3 times the median
stage (frequency), and (5) number of reversals, or when flow rates
change from rising to falling or vice versa, (rate of change). For all
flow metrics, we substituted stream stage, or the depth of water present
as a suitable proxy for discharge (Booth and Konrad 2017). All metrics
were calculated with the camera stream stage data and compared for
consistency with sensor data where applicable. We considered a no-flow
day to be any calendar day in which there was no flow observed (i.e.,
the riffle was dry, for the entire 24-hour period).
We compared the metrics among the nine sites to characterize variation
across intermittent streams. We converted date of first no flow period
to a Julian date and centered and scaled each of the flow metrics prior
to analysis. To visualize differences among sites and the relative
importance of each metric in describing variation across sites, we
constructed a Principal Component Analysis (PCA) in the vegan package of
R (Oksanen et al. 2022; R Core Team Version 4.2.2, 2022).
To explore the role of physical watershed characteristics on flow
classifications (completely dry, isolated pools, or connected), we
evaluated how four environmental covariates related with whether streams
fully dried or dried to isolated pools. Stream segment slope and depth
to restrictive layer were highly correlated (>0.65) with
other covariates and were removed from all analyses. We evaluated the
group means for this analysis, constructing boxplots to visualize
differences among groups. We used a Mann-Whitney U Test to compare
medians among groups.
We next explored the relationships between physical predictor variables
(watershed area, mean slope, percent forested, and mean depth to water
table) and flow metrics. Because this is an exploratory analysis and due
to small sample sizes, we evaluated relationships among flow metrics and
landscape variables by calculating correlation correlations for all
combinations of metrics and covariates. When data was available, we used
all ten sites to evaluate correlations, however only 9 sites had
complete data for some variables.