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Nitrate, Total Ammonia, and Total Suspended Sediments Modeling for the Mobile River Watershed

Author

Listed:
  • Vladimir J. Alarcon

    (Universidad Diego Portales, Civil Engineering School, Santiago, Chile)

  • Gretchen F. Sassenrath

    (Kansas State University, Parsons, Southeast Research and Extension Center, Kansas, USA)

Abstract

This paper presents details of a water quality model of the Mobile River watershed that estimates total suspended sediments at the outlet of the watershed. The model is capable of simulating Nitrate (NO3), Total Ammonia (TAM), and Total Suspended Sediments (TSS) for extended periods of time at a daily temporal resolution (1970-1995). The Hydrological Simulation Program Fortran is used for modeling the hydrological, nitrogenous constituents, and sediment processes. Based on the nutrient simulation and exploration of the effects of two management practices (filter strips and stream bank stabilization and fencing) on nutrient removal, the resulting sediment model is used to implement the most efficient nutrient management practice and explore its effects on TSS concentrations in the Mobile River. Results show that the implementation of the management practice “stream bank stabilization and fencing” to agricultural lands in sub-watersheds that had intense agricultural activities produced the highest reductions of NO3 concentration (up to 14.06%) and TAM concentrations (8.01%). Based on the nutrient simulation and identification of “stream bank stabilization and fencing” as the most efficient BMP for nutrient concentration reduction, the sediment model was used to explore its effects on TSS concentrations in the Mobile River. Implementing “stream bank stabilization and fencing” produced monthly median TSS concentration reductions ranging from 3.6% to 10.6% in the Mobile River.

Suggested Citation

  • Vladimir J. Alarcon & Gretchen F. Sassenrath, 2017. "Nitrate, Total Ammonia, and Total Suspended Sediments Modeling for the Mobile River Watershed," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 8(2), pages 20-31, April.
    • Handle: RePEc:igg:jaeis0:v:8:y:2017:i:2:p:20-31
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    Cited by:

    1. Vladimir J. Alarcon, 2021. "Hindcasting and Forecasting Total Suspended Sediment Concentrations Using a NARX Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-18, January.

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