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Modeling and Simulating Nutrient Management Practices for the Mobile River Watershed

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Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

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Abstract

In this research, an existing hydrological model of the Mobile River watershed is expanded to include water quality modeling of Nitrate (NO3) and Total Ammonia (TAM). The Hydrological Simulation Program Fortran is used for modeling the hydrological and the water quality processes. The resulting water quality model is used to implement nutrient management practices scenarios and, via simulation, explore the effects of those management scenarios at the most downstream river (Mobile River). Results show that the implementation of reported Best Management Practices (BMPs) at sub-watershed level (filter strips, stream bank stabilization and fencing) do not work as efficiently as when applied to the entire Mobile River watershed. Removal efficiencies reported for those BMPs at the sub-watershed scale ranged between 10.6 % and 54.0 %. When Filter Strips were applied to agricultural lands throughout the watershed, reductions of NO3 concentrations ranged from 1.48 % to 12.24 % and TAM concentrations were reduced between 0.84 % and 6.97 %. Applying Stream Bank Stabilization and Fencing to the whole watershed produced removals of NO3 of up to 14.06 %, and maximum TAM reductions of 8.01 %. The reasons for the discrepancy may be due to the site-specificity of the BMP techniques. This may preclude extrapolating those BMPs to sites where the characteristics are different (topography, soils, stream regime, etc.) reducing the effect of the applied BMPs. Watershed-wide aspects such as sub-basin-to-sub-basin or stream-to-stream interactions, may also reduce the effect of the management practices.

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Acknowledgment

This research was funded by a grant from CONICYT (REDES 140045).

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Correspondence to Vladimir J. Alarcon .

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Alarcon, V.J., Sassenrath, G.F. (2016). Modeling and Simulating Nutrient Management Practices for the Mobile River Watershed. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-42111-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42110-0

  • Online ISBN: 978-3-319-42111-7

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