Effect of water sampling strategies on the uncertainty of phosphorus load estimation in subsurface drainage discharge

Accurate phosphorus (P) load estimation in subsurface drainage water is critical to assess the field‐scale efficacy of conservation practices. The HydroCycle‐PO4 instrument measures real‐time total reactive P (TRP) concentration without the need for sample filtration, thereby enabling comparative evaluation of different sampling strategies. The main objective of this study was to evaluate the effects of water sampling strategies on the uncertainty of P load estimation. Hourly TRP concentration and hourly drainage discharge measurements formed the reference P load dataset. Four hypothetical water sampling strategies were evaluated: (a) time‐proportional discrete sampling, (b) time‐proportional composite sampling, (c) flow‐proportional discrete sampling, and (d) flow‐proportional composite sampling. All sampling strategies underestimated TRP load compared with the reference dataset. Total reactive P load underestimation changed from 0.2 to 51% as time‐proportional discrete sampling intervals increased from 3 h to 14 d. Total reactive P load underestimation changed from 12 to 43% as the time‐proportional compositing scenario increased from 1 to 7 d, each with one aliquot per day. In the case of flow‐proportional discrete sampling scenario, the lowest (0.6%) and the highest (–5.1%) uncertainties were observed when 1‐ and 5‐mm flow intervals were used. The relative error based on the results provided by the flow‐proportional composite sampling ranged from 0.2% when using 1‐mm flow interval to –6.7% when using 5‐mm flow interval. In conclusion, the flow‐proportional sampling strategies provided a more accurate estimate of cumulative P load with fewer number of samples because a greater portion of samples were taken at higher flow rates compared with time‐proportional sampling strategies. Core Ideas The purpose of the monitoring project should dictate the sampling strategy. All sampling strategies underestimated TRP load compared with the reference dataset. An increase in time‐proportional discrete sampling interval increased TRP load error. The flow‐proportional sampling strategies needed fewer samples. The flow‐proportional sampling strategy was more advantageous than other strategies.