Large variability of nitrate load estimated from sparse measurements by typical methods in Atlantic Canada
Nitrogen pollution in aquatic ecosystems, primarily from agricultural sources, presents significant environmental challenges. At the land management decision level, reducing nitrate leaching requires knowledge of nitrate loading over time and location, the complexity of which is amplified by limited data availability, especially in poorly gauged watersheds. This issue is particularly pronounced in cold and humid regions where water quality data are often collected during the growing season only. Large data gaps result in systematic errors when estimating nitrogen load based on traditional regression methods. In this study, we explore the feasibility of using process-based hydrologic model to estimate nitrate loads from sparse temporal water quality data in a coastal agricultural watershed in Atlantic 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 (as high as 21%) in load estimation by regression methods. In contrast, nitrate load estimates obtained with the Soil and Water Assessment Tool (SWAT) 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.
Keywords: nitrate load, sparse data, water quality, regression model, physical-based model
