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

Abstract

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 À0.8 and À0.4 mgL À1 ), and higher chlorophyll values (2.0 μgL À1 ) 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 weatherinduced 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.

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

“…In this case, more than reporting on the wide spectrum of possible sampling frequencies that are available nowadays (up to 1,024 Hz for microstructure purposes, see e.g., Kolås et al (2022), but the choice is dependent on the compromise between desired resolution of the final data set and available storage capacity), it is probably more relevant commenting on the advantages introduced by automated stations in terms of limiting the well-recognized influence of weather conditions on the availability/quality of the final measurements. On the one hand manual measurements suffer from the so-called "fair weather bias," where manual sampling is avoided or impossible in bad weather conditions, and on the other hand the quality and accuracy of these measurements, when their acquisition is logistically possible, are affected by the weather conditions (Rand et al, 2022). However, it should be noted that the availability of continuous, automatic in situ monitoring can be affected by the presence of harsh weather/climate conditions: a clear example is the removal of surface buoys from the Laurentian Great Lakes when they freeze in winter to avoid risk of damage by ice (e.g., Van Cleave et al, 2014).…”

Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods

“…However, traditional mechanism models also possess certain limitations. Firstly, they typically require extensive input data and parameters, including flow rate, rainfall, sediment characteristics, etc., which entail complex data acquisition and processing [ 10 ]. Secondly, establishing and calibrating the model necessitate deep professional knowledge and a substantial volume of measured data, demanding high technical expertise [ 11 ].…”

Water Quality Sampling and Multi-Parameter Monitoring System Based on Multi-Rotor UAV Implementation

“…Currently in most regions, water quality monitoring relies on traditional methods of manual sampling and laboratory testing [13,14]. Sampling personnel collect samples using boats to reach the target water areas.…”