A lagged variable model for characterizing temporally dynamic export of legacy anthropogenic nitrogen from watersheds to rivers

Environ Sci Pollut Res Int. 2015 Aug;22(15):11314-26. doi: 10.1007/s11356-015-4377-y. Epub 2015 Mar 25.

Abstract

Legacy nitrogen (N) sources originating from anthropogenic N inputs (NANI) may be a major cause of increasing riverine N exports in many regions, despite a significant decline in NANI. However, little quantitative knowledge exists concerning the lag effect of NANI on riverine N export. As a result, the N leaching lag effect is not well represented in most current watershed models. This study developed a lagged variable model (LVM) to address temporally dynamic export of watershed NANI to rivers. Employing a Koyck transformation approach used in economic analyses, the LVM expresses the indefinite number of lag terms from previous years' NANI with a lag term that incorporates the previous year's riverine N flux, enabling us to inversely calibrate model parameters from measurable variables using Bayesian statistics. Applying the LVM to the upper Jiaojiang watershed in eastern China for 1980-2010 indicated that ~97% of riverine export of annual NANI occurred in the current year and succeeding 10 years (~11 years lag time) and ~72% of annual riverine N flux was derived from previous years' NANI. Existing NANI over the 1993-2010 period would have required a 22% reduction to attain the target TN level (1.0 mg N L(-1)), guiding watershed N source controls considering the lag effect. The LVM was developed with parsimony of model structure and parameters (only four parameters in this study); thus, it is easy to develop and apply in other watersheds. The LVM provides a simple and effective tool for quantifying the lag effect of anthropogenic N input on riverine export in support of efficient development and evaluation of watershed N control strategies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Bayes Theorem
  • China
  • Eutrophication
  • Humans
  • Models, Statistical
  • Nitrogen / analysis*
  • Rivers / chemistry*
  • Water Quality

Substances

  • Nitrogen