Citation:

Kamrath, B., Y. Yuan, N. Manning, AND L. Johnson. Influence of sampling frequency and estimation method on phosphorus load uncertainty in the Western Lake Erie Basin, Ohio, USA. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, 617(B):128906, (2023). https://doi.org/10.1016/j.jhydrol.2022.128906

Impact/Purpose:

Water bodies and coastal areas around the world are threatened by excessive amounts of nitrogen (N) and phosphorous (P) from upstream watersheds, which can cause rapid proliferation of algae. These algal blooms negatively impact drinking water sources, aquatic species, and recreational services of water bodies by producing toxins, also called harmful algal blooms (HABs). Finding ways reducing N and P losses from agricultural runoff is paramount important for EPA program offices and regional partners to make informed decisions to better control nutrient losses from agricultural-dominated watershed.

Description:

Accurate estimates of nutrient loads are necessary to identify critical source areas and quantify the impact of management practices on pollutant export. Previous studies have investigated nutrient load estimate uncertainty, but they often focus on nutrient loads estimated using an interpolation method for large-scale watersheds with short-term datasets. The study objective was to quantify uncertainty in soluble reactive phosphorus (SRP), total phosphorus (TP), and suspended solids (SS) load estimates from two small (<103 km2) agricultural watersheds in the western Lake Erie Basin resulting from different sampling frequencies. Each watershed had high temporal resolution datasets of discharge (15 min) and nutrient concentration (1 to 3 samples per day) collected over a 30-year period (1990–2020). Firstly, SRP, TP, and SS loads were calculated using the high temporal resolution datasets, which was assumed as “true loads”. Secondly, the high temporal concentration data were decomposed to semiweekly, weekly, biweekly, and monthly sampling and annual loads were estimated using four common load estimation methods to assess the effect of sampling frequency and load estimation method on load estimate error. Across the four different methods, the composite method had the lowest relative root mean square and absolute bias, but the rectangular interpolation method was the most precise. However, even with semiweekly sampling, the composite method resulted in an unacceptable level of precision (average imprecision = 39 %), while the interpolation method resulted in an unacceptable bias (average absolute bias = 16 %). Because neither method could provide acceptable accuracy and precision at the lowest decrease in sampling (e.t. semiweekly sampling), continued daily sampling is recommended in these watersheds.

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