Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling

• Main Authors: J. W. Miller, K. Karimi, A. Sankarasubramanian, D. R. Obenour
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-05-01
• Series: Hydrology and Earth System Sciences
• Online Access: https://hess.copernicus.org/articles/25/2789/2021/hess-25-2789-2021.pdf

<p>Excessive nutrient loading is a major cause of water quality problems worldwide, often leading to harmful algal blooms and hypoxia in lakes and coastal systems. Efficient nutrient management requires that loading sources are accurately quantified. However, loading rates from various urban and rural non-point sources remain highly uncertain especially with respect to climatological variation. Furthermore, loading models calibrated using statistical techniques (i.e., hybrid models) often have limited capacity to differentiate export rates among various source types, given the noisiness and paucity of observational data common to many locations. To address these issues, we leverage data for two North Carolina Piedmont river basins collected over three decades (1982–2017) using a mechanistically parsimonious watershed loading and transport model calibrated within a Bayesian hierarchical framework. We explore temporal drivers of loading by incorporating annual changes in precipitation, land use, livestock, and point sources within the model formulation. Also, different representations of urban development are compared based on how they constrain model uncertainties. Results show that urban lands built before 1980 are the largest source of nutrients, exporting over twice as much nitrogen per hectare than agricultural and post-1980 urban lands. In addition, pre-1980 urban lands are the most hydrologically constant source of nutrients, while agricultural lands show the most variation among high- and low-flow years. Finally, undeveloped lands export an order of magnitude (<span class="inline-formula">∼7</span>–<span class="inline-formula">13×</span>) less nitrogen than built environments.</p>