Estimating stream solute loads from fixed frequency sampling regimes: the importance of considering multiple solutes and seasonal fluxes in the design of long‐term stream monitoring networks

Abstract

Abstract:Reduced sampling frequency is known to increase the error associated with estimates of stream solute load. However, the extent to which the magnitude of error differs among commonly measured solutes and across seasons is unclear. In this study, a high sampling frequency data set from two forested streams (one upland-draining and one wetland-draining stream) in south-central Ontario was systematically sub-sampled to simulate weekly, fortnightly and monthly fixed frequency sampling regimes for 12 stream solutes. We found that solutes which had a higher degree of temporal variation in concentration (i.e. higher %RSD) had poorer precision (C v ) in estimates of annual load relative to solutes with a lower %RSD. In addition, the magnitude and direction of bias varied considerably among solutes and were related to differences in spring concentration-discharge relationships (m [spring Q vs C] ) among the 12 solutes. Solutes which decreased in concentration with increases in spring flow (i.e. m [spring Q vs C] <0) exhibited positive bias in annual load while solutes which increased in concentration with increases in spring flow (i.e. m [spring Qvs C] >0) were negatively biased. In terms of differences between seasonal and annual load errors, precision was generally lower for estimates of seasonal load relative to annual load while bias varied in both magnitude and direction among seasons. When the root mean square error (RMSE) of load estimates was compared to a threshold of acceptable error (<15%), the proportion of solutes attaining acceptable levels of uncertainty ranged from 11/12 for annual load estimates at a weekly sampling frequency to only 4/12 at a monthly frequency when both annual and seasonal loads were considered. Our results demonstrate that commonly measured solutes do not behave uniformly in response to changes in sampling frequency and that estimates of seasonal loads are often less accurate than estimates of annual load. These findings provide important insights into the design of stream monitoring programs and the evaluation of existing long-term data sets.

Approaches to stream solute load estimation for solutes with varying dynamics from five diverse small watersheds

“…Good precisions could be achieved except when concentrations were variable (relative SEs of the concentrations were >~20%) and when there were not strong temporal patterns in concentrations (autocorrelation of the concentrations was <~0.3–0.4); and are consistent with the findings of Kerr et al. ().…”

“…Kerr et al. () indicated that solutes that had higher temporal variation in concentrations had poorer precisions when using a period‐weighted approach. Aulenbach () indicated that for suspended sediment with a relatively strong concentration–discharge relation, when the autocorrelation of regression‐model residual concentrations was >0.15, the composite method could improve upon regression‐model method load estimates.…”

Quantification of Phosphorus Exports from a Small Forested Headwater-Catchment in the Eastern Ore Mountains, Germany

“…It was observed that the event-based export of P can contribute up to 40% to the seasonal and 19% to the annual export fluxes, which highlights the importance of monitoring stormflow events for flux estimation and process understanding. These results support the findings of numerous studies [9,11,18,31] which have found that time discrete sampling schemes at the monthly or bi-weekly time scale are unable to capture short-term changes in solute concentrations, and therefore can lead to a bias in load calculations. Ide et al [18] suggested that a certain number of monitored stormflow events are necessary to quantify export fluxes with low bias.…”

“…Using this relationship, the solute concentration for the times without samples is estimated based on the measured discharge. The extrapolation methods differ in the method used to establish the discharge-solute relationship [9,20]. The interpolation models shown in Table 1 were applied using the R-package loadflex [20].…”

“…As with other solutes, the quantification of P fluxes across varying spatial scales requires the multiplication of measured water volumes with solute concentrations over a distinct time period. This quantification process has inherent uncertainties due to a number of factors, including the (lack of) precision in water flux measurements [9,10], uncertainties related to the measurement of solute concentration [9], uncertainty related to the frequency of solute sampling [11][12][13], and the uncertainty related to the load calculation method [9,14,15]. These uncertainties can lead to substantial differences in load estimations, which can range between 5% and 12% depending on the respective solute [10].…”