long-term (LT) stream chemistry studies are used to examine changes in and responses to the environment. Much of the data collected over long periods of time goes through changes in instrumentation, methods, and personnel potentially resulting in changing values. A data user must understand these measures of data quality through quality control (QC) results to know with certainty if trends are real or attributable to other factors. We used the Web of Science database search engine to search for LT stream chemistry studies. For each study, we then determined: record or study length; if QC was reported; and if QC was used. We found that 33% of papers reported QC in the method, and 12% presented the QC in the results. Next, we conducted a case study on 46 years of stream chemistry data to evaluate the data with and without the application of QC protocols from two watersheds (WS) at Coweeta Hydrologic Laboratory; WS 7; clear-cut in 1967–77 and adjacent WS 2 which serves as a reference. We focused on nitrogen and sulfur due to their importance in understanding the forest ecosystem response to disturbance (NO3) and acid deposition (SO4). We determined average annual dissolved inorganic nitrogen (DIN) export (NH4 þ NO3 ¼ DIN) using three methods for censoring values below the method detection limit (mdl): (1) the found value, (2) the value of zero, and (3) one-half the mdl value. We found that DIN export for WS 2/WS 7 was (1) 66.9/831.4 (g ha1 yr1 ), (2) 45.4/808.0 (g ha1 yr1 ), and (3) 72.1/823.2 (g ha1 yr1 ) using the three censoring methods, and that the export estimate was significantly different for WS 2 but not for WS 7 (P ¼ 0.001). We found that on average stream NH4 concentrations were below the mdl 58% of the time until an instrument change in 1994 resulted in improved mdls resulting in fewer data points below detection. We found increased bias for stream SO4 concentration following an instrumentation change from segmented flow analysis to ion chromatography. As a result, stream SO4 concentration data that were bias-corrected declined more rapidly in WS 2 compared with non-bias-corrected data, but not in WS 7. We conclude that including QC results with LT data is essential to verify data validity and give the data user a full understanding of the results.
Brown, Cindi L.; Miniat, Chelcy F.; Knoepp, Jennifer D. 2021. Investigating the effect of lab bias on long-term stream chemistry data. Hydrology Research. 149(3): 141-. https://doi.org/10.2166/nh.2021.164.