INTRODUCTION
Data documenting the temporal variation of solute concentrations in
streams have advanced the knowledge about how catchments produce stream
flow and impact streamwater quality. Lab analysis of discrete water
samples has underlain this knowledge such as how piston displacement of
stored water is a dominant stream flow generation mechanism in many
catchments (Sklash and Farvolden 1979), how mineral weathering and soil
development are impacted by human activities (Bailey 2020), and how
hydrology and soil geochemistry interact to regulate streamwater solutes
(e.g., Bennetin et al., 2015), just to name a few examples. However,
weekly or less frequent sampling can inadequately capture temporal
patterns in the variation of streamwater chemistry (Kirchner 2004).
Advancements in technology have allowed us to supplement the information
gained from water sampling with high-frequency data from electronic
sensors (e.g., Pellerin et al. 2010; von Freyberg et al. 2017).
Stream solute data allow estimation of the fractional contribution of
new precipitation versus older, stored water to streamflow (Hooper and
Shoemaker 1986; Pellerin et al. 2008). This chemical hydrograph
separation can provide insights into how catchments transport rainfall
and how catchment characteristics or human modifications alter this
hydrologic transport (Pellerin et al. 2008; Inserillo et al. 2107).
Specific electrical conductivity (SC) is a common solute indicator used
for ”new water” estimates (Sklash and Farvolden 1979; Pellerin et al.
2008). Sensors measuring SC are robust, relatively affordable, and have
shown that stream chemistry is highly variable on short time scales
(Walling 1975; Vogt et al. 2010). Analysis of temporal SC patterns
remains an opportunity for advancing catchment hydrology and
biogeochemistry. Improved methods for analysis of these high-frequency
time series can help expand our understanding of water movement and
solute transport through catchments.
During and immediately following storm events, the SC of streams
exhibits patterns that provide insights into catchment hydrology and
biogechemistry. During a storm, streamwater SC is diluted by rainwater
with lower SC than the groundwater that is sourcing streamwater prior to
the event (Sklash and Farvolden 1979). Following this, barring further
precipitation events, the rainwater continues to drain from the
catchment, is evapotranspired, or is integrated into stores, causing SC
to recover towards its pre-storm conditions (Walling 1975). Within
certain catchments, a solute flush occurs before dilution (Inamdar et
al. 2009; Inserillo et al., 2017). Specific conductivity drastically
increases on the rising limb of some streams’ hydrographs, indicating a
flush of ions entering the stream. This flush may represent a distinct
source of water entering the stream beyond that of mean stored
groundwater and rainwater (Walling 1975, Creed and Band 1998, Inserillo
et al. 2017). Pairing meteorologic conditions with sensed,
high-frequency time series of stream solutes will aid understanding how
precipitation, antecedent hydrologic conditions, and seasonal changes
influence the variability of those solutes (e.g., Robson 1992, Biron et
al. 1999, Kirchner 2004, Fovet et al. 2018).
The goal of this study was to analyze the temporal behavior of
streamwater SC in response to a variety of different precipitation
events. To this end, high frequency data from an in situ sensor
deployment were used to analyze the magnitude of solute flushing
(FSC), the magnitude of SC dilution
(DSC), and the rate at which SC recovers after a
dilution occurs (RSC). We developed an algorithm to
objectively analyze these chemograph patterns and produce functional
relationships between storm characteristics and stream SC behavior.