Unveiling biases in water sampling: A Bayesian approach for precision in edge-of-field monitoring.

Edge-of-field (EoF) water sampling methods play a crucial role in understanding non-point source nutrient fate and its environmental impacts, yet accurately interpreting water quality studies, remains challenging. This study evaluates and compares four EoF runoff water sampling techniques: (1) a commercial automated sampler (ISCO) with hourly sampling, (2) a low-cost internet of things sampler low-cost sampler with hourly sampling, (3) hourly hand sampling (grab hourly sampling), and (4) intermittent grab sampling (GB) in 2023 and 2024 at a surface irrigated agricultural site in Fort Collins, Colorado involving three levels of tillage intensity. Nine water quality parameters (nitrate-N, nitrite-N, total Kjeldahl nitrogen, orthophosphate-P, total phosphorus (TP), total suspended solids (TSS), total dissolved solids, pH, and specific conductivity) were measured over nine irrigation-driven and two rainfall storm runoff events. Resulting concentration values were modeled simultaneously using a Bayesian hierarchical generalized linear mixed model, enabling causal inference with uncertainty quantification while accommodating for missing data. Results show strong alignment across samplers for most analytes, confirming the validity of integrating diverse methods in long-term and widespread monitoring. However, ISCO samples exhibited consistently elevated TSS and TP due to a purge-induced sediment plume from the flume's stainless-steel bottom intake; excluding the first ISCO sample of each pair of sample draws restored agreement with other methods. These findings show the importance of flume morphology, intake placement, purge protocol, and selective data exclusion (if necessary) to ensure comparability across sampling methods.