Temporal scales driving streamwater in organic monomeric aluminum variability in the White Mountain National Forest, New Hampshire
Ellis, Carolyn June
Inorganic monomeric aluminum (Ali) is toxic to aquatic organisms and while many samples are taken at weekly or monthly intervals, that frequency is likely not high enough to understand temporal variation in certain water quality parameters, like Ali. Land managers often face the difficult task of protecting aquatic organisms from toxic streamwater environments that are nearly impossible to monitor continuously. This study investigated the temporal scales of variability in dissolved Ali at four streams in the White Mountain National Forest in New Hampshire to provide managers and scientists with a better understanding of Ali dynamics. We used data from intensively sampled hydrologic events during 2015 in addition to five to six years of weekly to monthly data. We quantified the proportion of variability attributed to each of three temporal components: multi-year trends, seasonality, and event scale. To do this, we extracted a trend or pattern from each temporal scale, a linear concentration-discharge relationship in the case of hydrologic events, and compared the results quantitatively and objectively. This analysis revealed that seasonality or hydrologic events explain the most variability in Ali, depending on site, while multi-year trends explain only 11% or less of the variability at each site. The amount of Ali variability that remains unexplained after the three temporal scales were accounted for is greater than 50% at each site. We then examined time series of events and hysteresis effects as a method of exploring the remaining variability. This exploratory analysis showed that removing multi-year, seasonal, and concentration-discharge trends minimally reduced variability at the event scale. While the overall concentration-discharge relationship at each site appeared to be linear, the relationships are often non-linear at the event scale. Residual variability may stem from lower frequency sampling being unable to account for more complicated, cyclical patterns seen at finer temporal scales. Results from this study are informative for managers who want to create efficient sampling regimes that will effectively capture a stream's Ali conditions, but who may not have the time and resources for continuous high-frequency monitoring. If lacking the resources for such intensive monitoring, characterizing a stream's Ali, based on our results, may be best done with samples across different hydrologic conditions, as well as across during different seasons.