Figure Captions and Tables
Figure 1: A conceptual model of the specific conductivity (SC) patterns
that streamwater takes during and after a precipitation event.
Highlighted are the aspects that we use to characterize the watershed
response to individual precipitation events: the flush, dilution, and
rate of SC recovery. Also shown is the pre-storm trajectory, or the
course of streamwater SC prior to the influence of a precipitation
event, which is used to quantify both the magnitude of the flush and the
magnitude of the dilution.
Figure 2: A map of the Clay Brook catchment.
Figure 3: The full record of specific conductivity (SC) and
precipitation data for the months we analyzed in both 2013 and 2014.
Figure 4: A visual representation of the parameter ranges for the Monte
Carlo calculations. Parameter A defined the minimum amount of
precipitation for an event to be considered for analysis. Parameter B
defined both the time between individual precipitation measurements that
separated unique events, and the range of time to define the pre-storm
trajectory (PST). Parameter D defined the amount of time for a flushing
response to be found after the storm began. Parameter E defined the
amount of time for a dilution response to be found after the flush
occurred, or after 6 hours prior to the end of the storm’s precipitation
if no substantial flush was present. Parameter F defined the amount of
time in which the slope of the rate of specific conductivity (SC)
recovery was calculated after the dilution. Parameter G defined the
number of standard deviations above or below the PST line that a peak
flush or maximum dilution must have exceeded in order to be declared
substantial enough for analysis.
Figure 5: The algorithm-derived points of peak flush and maximum
dilution for a precipitation event beginning on May 8, 2013. and their
respective distributions. Of note are the three points in which a peak
flush was found across iterations, due to the variable range of
Parameter D, and the increasing specific conductivity (SC) values of
those points occurring in time. In this way, the algorithm seeks the
truest representation of a flush over time. A) The distributions of
algorithm derived peak flush values (number of times a peak flush was
detected / number of times the precipitation event was detected). B) The
median of the extracted peak flush and maximum dilution values from each
of the algorithm’s iterations. These points overlay the SC data
surrounding the precipitation event. The pre-storm trajectory (PST) and
rate of SC recovery (RSC) are also displayed with
estimated intercepts for appropriate visual representation. C) The
distribution of algorithm derived maximum dilution values (number of
times a maximum dilution was detected / number of times the
precipitation event was detected).
Figure 6: The algorithm-derived points of peak flush and maximum
dilution for a precipitation event beginning on October 4, 2014. and
their respective distributions. Unlike Figure 5, the earlier point for
peak flush is chosen more frequently by the algorithm because the first
has a greater specific conductivity (SC). In this way, the algorithm
retains the truest representation of a flush despite later options being
available. A) The distributions of algorithm derived peak flush values
(number of times a peak flush was detected / number of times the
precipitation event was detected). B) The median of the extracted peak
flush and maximum dilution values from each of the algorithm’s
iterations. These points overlay the SC data surrounding the
precipitation event. The pre-storm trajectory (PST) and rate of SC
recovery (RSC) are also displayed with estimated
intercepts for appropriate visual representation. C) The distribution of
algorithm derived maximum dilution values (number of times a maximum
dilution was detected / number of times the precipitation event was
detected).
Figure 7: Kendall’s τ relationships for the magnitude of peak flush
(FSC) and A) precipitation intensity
(IP), B) maximum precipitation intensity
(IP,max), and C) total precipitation
(PT). Whiskers indicate the interquartile range of the
values obtained from the 10,000 iterations. An absence of whiskers
indicates 100% confidence in the value across all
iterations.
Figure 8: Kendall’s τ relationships for the magnitude of maximum
dilution (DSC) and A) precipitation intensity
(IP), B) maximum precipitation intensity
(IP,max), and C) total precipitation
(PT). Whiskers indicate the interquartile range of the
values obtained from the 10,000 iterations. An absence of whiskers
indicates 100% confidence in the value across all
iterations.
Figure 9: Kendall’s τ relationships for the rate of specific
conductivity recovery (RSC) and A) precipitation
intensity (PT) and B) magnitude of the dilution
(DSC). Whiskers indicate the interquartile range of the
values obtained from the 10,000 iterations. An absence of whiskers
indicates 100% confidence in the value across all iterations.
Table 1: Ranges for each parameter’s uniform distribution. The
PT is the total precipitation for a storm, the
RSC is the recovery rate of SC after peak dilution, and
PST is the pre-storm trajectory.