@article{karimi_obenour_2024, title={Characterizing Spatiotemporal Variability in Phosphorus Export across the United States through Bayesian Hierarchical Modeling}, ISSN={["1520-5851"]}, url={https://doi.org/10.1021/acs.est.3c07479}, DOI={10.1021/acs.est.3c07479}, abstractNote={Phosphorus inputs from anthropogenic activities are subject to hydrologic (riverine) export, causing water quality problems in downstream lakes and coastal systems. Nutrient budgets have been developed to quantify the amount of nutrients imported to and exported from various watersheds. However, at large spatial scales, estimates of hydrologic phosphorus export are usually unavailable. This study develops a Bayesian hierarchical model to estimate annual phosphorus export across the contiguous United States, considering agricultural inputs, urban inputs, and geogenic sources under varying precipitation conditions. The Bayesian framework allows for a systematic updating of prior information on export rates using an extensive calibration data set of riverine loadings. Furthermore, the hierarchical approach allows for spatial variation in export rates across major watersheds and ecoregions. Applying the model, we map hotspots of phosphorus loss across the United States and characterize the primary factors driving these losses. Results emphasize the importance of precipitation in determining hydrologic export rates for various anthropogenic inputs, especially agriculture. Our findings also emphasize the importance of phosphorus from geogenic sources in overall river export.}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Karimi, Kimia and Obenour, Daniel R.}, year={2024}, month={May} }
 @article{karimi_miller_sankarasubramanian_obenour_2023, title={Contrasting Annual and Summer Phosphorus Export Using a Hybrid Bayesian Watershed Model}, volume={59}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2022WR033088}, DOI={10.1029/2022WR033088}, abstractNote={Abstract}, number={1}, journal={WATER RESOURCES RESEARCH}, author={Karimi, K. and Miller, J. W. and Sankarasubramanian, A. and Obenour, D. R.}, year={2023}, month={Jan} }
 @article{miller_karimi_sankarasubramanian_obenour_2021, title={Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling}, volume={25}, ISSN={["1607-7938"]}, url={https://doi.org/10.5194/hess-25-2789-2021}, DOI={10.5194/hess-25-2789-2021}, abstractNote={Abstract. 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
(∼7–13×) less nitrogen than built environments.
                    }, number={5}, journal={HYDROLOGY AND EARTH SYSTEM SCIENCES}, author={Miller, Jonathan W. and Karimi, Kimia and Sankarasubramanian, Arumugam and Obenour, Daniel R.}, year={2021}, month={May}, pages={2789–2804} }