Uncertainty in nitrate load calculations evaluated using Monte Carlo simulations based on in-situ nitrate sensors in two Danish headwater streams

作者：Sofie Gyritia Weitzmann van't Veen, S. Larsen, Peter Mejlhede Andersen, Niels Bering Ovesen, Jane R. Laugesen, Brian Kronvang · 发表于：Journal of Hydrology · 年份：2025 · DOI：10.1016/j.jhydrol.2025.133816 · 被引用次数：2 · 研究领域：Soil and Water Nutrient Dynamics、Hydrology and Watershed Management Studies、Marine and coastal ecosystems

This study used high-frequency (HF) nitrate-nitrogen (NO3-N) sensor data to explore the uncertainty of calculating NO3-N loads in two headwater streams (Horndrup and Lyby-Grønning) with infrequent sample collection. Accurate annual and monthly N load estimates are crucial for cost-efficient management of N in catchments and for correct calibration and validation of catchment models like SWAT. Ultraviolet (UV) NO3-N sensors were installed in the two agricultural headwater streams for two hydrological years (June 2021 to May 2023) to measure NO3-N concentrations every minute. The cleaned dataset was used to investigate NO3-N load estimates using a Monte Carlo approach, with 1000 simulations for five infrequent sampling strategies: daily, weekly, fortnightly, 18 annual samples, and monthly samples. Bias (Flux Bias Statistic), precision (standard deviation), and total uncertainty (RMSE) were calculated for the annual and monthly NO3-N loads. Richards-Baker Flashiness Index (RBI) was used to explore the influence of hydrology on differences between HF and infrequent sampling. The sampling strategy needed to meet a bias and achieve RMSE <2 %, 5 %, and 10 % for monthly loads was explored. The uncertainty of the load estimates increased with fewer grab samples. The bias and RMSE of the annual load estimates from the five strategies were <±7.7 % and <10.3 %, respectively. Monthly NO3-N loads had higher uncertainties, with average bias and RMSE of ±4 % and 15 % (max. −13 % and 28 %) in...