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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #416377
Research Project: Enhancing Agricultural Management and Conservation Practices by Advancing Measurement Techniques and Improving Modeling Across Scales

Location: Hydrology and Remote Sensing Laboratory

Title: Large variability of nitrate load estimation from sparse measurements by typical methods in Atlantic Canada

Author
item LIANG, K - University Of Maryland
item JIANG, Y - Agriculture And Agri-Food Canada
item FULLER, K - Agriculture And Agri-Food Canada
item CORDEIRO, M - University Of Manitoba
item Zhang, Xuesong
item QI, J - University Of Maryland
item GENG, X - Agriculture And Agri-Food Canada
item LIU, T - Michigan Technological University
item ZHANG, Q - University Of Maryland
item AZIMI, M - University Of New Brunswick
item MENG, F - University Of New Brunswick

Submitted to: Frontiers in Environmental Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/13/2024
Publication Date: 3/7/2024
Citation: Liang, K., Jiang, Y., Fuller, K., Cordeiro, M., Zhang, X., Qi, J., Geng, X., Liu, T., Zhang, Q., Azimi, M.A., Meng, F.R. 2024. Large variability of nitrate load estimation from sparse measurements by typical methods in Atlantic Canada. Frontiers in Environmental Science. 13. Article e1557004. https://doi.org/10.3389/fenvs.2025.1557004.
DOI: https://doi.org/10.3389/fenvs.2025.1557004

Interpretive Summary: Precise quantification of nitrogen loads over time and space is essential for designing effective measures to reduce nitrogen pollution. This is challenging because of limited data, especially in cold, wet areas where water quality data is mostly collected during the growing season. These data gaps can lead to big errors in estimating pollution using simple statistical methods. This study looked at using the process-based SWAT model to estimate nitrate pollution in the Dunk River in Prince Edward Island, Canada. The SWAT model was compared to three statistical methods and found to be more accurate despite missing data during the non-growing season. The study shows that advanced models like SWAT are better for estimating nitrogen pollution when data is limited, which is important for managing water quality effectively.

Technical Abstract: Nitrogen pollution in aquatic ecosystems, primarily from agricultural sources, presents significant environmental challenges. Effectively reducing nitrate load requires precise quantification over time and space, which is often challenging due to limited data availability. For example, in cold and humid regions, water quality data are predominantly collected during the growing season. Large data gaps can result in systematic errors in estimating nitrogen loads based on regression methods. In this study we explored the feasibility of using a process-based hydrological model, Soil and Water Assessment Tool (SWAT) to estimate nitrate loads from sparse water quality data in the Dunk River watershed in Prince Edward Island (PEI), Canada, and compared its performance with three regression methods. We found that the absence of the available 16% non-growing season data during the 10-year study period can lead to significant biases in load estimation (as high as 21%) by regression methods. In contrast, nitrate load estimates obtained with the SWAT model were less sensitive to systematic data gaps. The results suggest that process-based models like SWAT can be a viable alternative for nitrate load estimation when limited data is available. As agri-environmental water quality issues become more pressing, it is crucial to use appropriate methods based on data quality and availability to avoid misleading results.