The human factor: Weather bias in manual lake water quality monitoring

• Published in: Limnology and oceanography, methods methods, 2022-05, Vol.20 (5), p.288-303
• Main Authors: Rand, James M., Nanko, Mirjam O., Lykkegaard, Mikkel B., Wain, Danielle, King, Whitney, Bryant, Lee D., Hunter, Alan
• Format: Article
• Language: English
• ISSN: 1541-5856; 1541-5856
• DOI: 10.1002/lom3.10488

Sampling bias due to weather conditions has been anecdotally reported; however, in this analysis we demonstrate that manual lake sampling is significantly more likely to take place in “fair weather” conditions. We show and quantify how a manual lake monitoring program in Maine, USA, is biased due to wind speed, rainfall intensity, and air temperature. Emulating a manually sampled water quality (WQ) data set, we show that, on average, manual sampling recorded, depending upon depth, higher water temperature (between 0.4°C and 1.2°C), lower dissolved oxygen (DO) (between −0.8 and −0.4 mgL−1), and higher chlorophyll values (2.0 μgL−1) than average automated monitoring. By analyzing the actual manual monitoring data, we show that manually collected lake water temperatures are on average 1.0°C higher in the epilimnion and 0.5°C (corrected for sensor lag) higher in the hypolimnion compared to those collected using automated methods. We attribute these differences in WQ measurement values to the weather‐induced manual sampling bias. We believe that the nature of weather bias on manual monitoring will always record higher water temperatures, higher chlorophyll, and lower DO than automatic monitoring. The methodologies presented in this study will apply to similar manually sampled lake monitoring programs and the manual sampling bias will likely affect other WQ parameters. The weather‐induced water temperature bias reported is of the same order of magnitude as the root mean square errors reported in many lake models and is therefore considered substantial. If generally applicable and not corrected for, these results will have important implications for climate models, and similar applications, where manually collected WQ data are employed.