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

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