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

Abstract

. 2016. Approaches to stream solute load estimation for solutes with varying dynamics from five diverse small watersheds. Ecosphere 7(6):e01298. 10. 1002/ecs2.1298 Abstract. Estimating streamwater solute loads is a central objective of many water-quality monitoring and research studies, as loads are used to compare with atmospheric inputs, to infer biogeochemical processes, and to assess whether water quality is improving or degrading. In this study, we evaluate loads and associated errors to determine the best load estimation technique among three methods (a period-weighted approach, the regression-model method, and the composite method) based on a solute's concentration dynamics and sampling frequency. We evaluated a broad range of varying concentration dynamics with stream flow and season using four dissolved solutes (sulfate, silica, nitrate, and dissolved organic carbon) at five diverse small watersheds (Sleepers River Research Watershed, VT; Hubbard Brook Experimental Forest, NH; Biscuit Brook Watershed, NY; Panola Mountain Research Watershed, GA; and Río Mameyes Watershed, PR) with fairly high-frequency sampling during a 10-to 11-yr period. Data sets with three different sampling frequencies were derived from the full data set at each site (weekly plus storm/snowmelt events, weekly, and monthly) and errors in loads were assessed for the study period, annually, and monthly. For solutes that had a moderate to strong concentration-discharge relation, the composite method performed best, unless the autocorrelation of the model residuals was <0.2, in which case the regression-model method was most appropriate. For solutes that had a nonexistent or weak concentration-discharge relation (model R 2 < about 0.3), the period-weighted approach was most appropriate. The lowest errors in loads were achieved for solutes with the strongest concentration-discharge relations. Sample and regression model diagnostics could be used to approximate overall accuracies and annual precisions. For the period-weighed approach, errors were lower when the variance in concentrations was lower, the degree of autocorrelation in the concentrations was higher, and sampling frequency was higher. The period-weighted approach was most sensitive to sampling frequency. For the regression-model and composite methods, errors were lower when the variance in model residuals was lower. For the composite method, errors were lower when the autocorrelation in the residuals was higher. Guidelines to determine the best load estimation method based on solute concentration-discharge dynamics and diagnostics are presented, and should be applicable to other studies.

Thirty‐year trends in acid deposition and neutralization in two headwater catchments, northwestern Massachusetts, USA

“…Values from White, Blum, Schulz, Huntington, and Peters () except SO 4 2− and SiO 2 , which are calculated from Aulenbach et al (, table 4); White et al report a value of 41.1 for SiO 2 .…”

“…Values from Hornbeck, Bailey, Buso, and Shanley () except SO 4 2− and SiO 2 , which are calculated from Aulenbach et al (, table 4).…”

Effects of instream processes, discharge, and land cover on nitrogen export from southern Appalachian Mountain catchments

“…We calculated annual export (load) of dissolved nitrogen components for each stream by period‐weighted trapezoidal integration: Using the weekly grab samples, the flow‐weighted average DIN concentration at the beginning and end of the week was multiplied by the total volume of flow during the week, and these weekly loads were summed for the year. Because weekly grab samples do not usually include storms, this method can underestimate or overestimate annual load if concentration increases or decreases during storms (e.g., Aulenbach et al, ), so for DIN, we used two additional methods to estimate annual export, LOADEST and load separation. For the USGS LOADEST program (Runkel et al, ), we used storm and grab sample concentrations and instantaneous streamflow data to calibrate load–discharge relationships for each stream and determine the adjusted maximum likelihood estimate of daily load.…”

Incorporating Rainfall-Runoff Events into Nitrate-Nitrogen and Phosphorus Load Assessments for Small Tile-Drained Catchments

“…For nitrate-N load estimation, two methods have been recommended by numerous authors: (1) linear interpolation, a period weighted method, and (2) flow-weighted mean concentration, an averaging method [18,24,25]. Previous research has shown that the uncertainty due to infrequent sampling and nutrient load estimation approaches for drainage systems or small streams is often much greater than the uncertainty brought about by other steps in the sample collection process [25][26][27].…”

“…Previous research has shown that the uncertainty due to infrequent sampling and nutrient load estimation approaches for drainage systems or small streams is often much greater than the uncertainty brought about by other steps in the sample collection process [25][26][27]. In general, the uncertainties in annual or monthly nutrient load estimates are reported to be influenced both by the sampling interval and the load assessment methods and tend to increase with an increasing sampling interval for the majority of load estimation algorithms [24,25,28]. Ratio methods are often reported as bei ng unsatisfactory compared with the two aforementioned simple methods.…”