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Water Resources Research

Development of updated algorithms to define particle dynamics in Lake Tahoe (CA-NV) USA for total maximum daily load

Authors

  • G. B. Sahoo,

    Corresponding author
    1. Tahoe Environmental Research Center, UC Davis, Davis, California, USA
    2. Department of Civil and Environmental Engineering, UC Davis, Davis, California, USA
    • Corresponding author: G. B. Sahoo, Tahoe Environmental Research Center, UC Davis, One Shields Ave., Davis, CA 95616, USA. (gbsahoo@ucdavis.edu)

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  • Daniel Nover,

    1. Department of Civil and Environmental Engineering, UC Davis, Davis, California, USA
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  • S. G. Schladow,

    1. Tahoe Environmental Research Center, UC Davis, Davis, California, USA
    2. Department of Civil and Environmental Engineering, UC Davis, Davis, California, USA
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  • J. E. Reuter,

    1. Tahoe Environmental Research Center, UC Davis, Davis, California, USA
    2. Department of Environmental Science and Policy, UC Davis, Davis, California, USA
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  • David Jassby

    1. Department of Civil and Environmental Engineering, UC Davis, Davis, California, USA
    2. Department of Chemical and Environmental Engineering, UC Riverside, Riverside, California, USA
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Abstract

[1] Two algorithms (the fractal (FPA) and the solid (SPA) particle aggregation) simulating fine particle dynamics were developed and incorporated in the Dynamic Lake Model with Water Quality (DLM-WQ). The previous aggregation model in DLM-WQ required a value for the probability of aggregation independent of particle size distribution (PSD) and concentration. In this study, we developed an algorithm that estimates the probability of aggregation based on PSD, sediment concentration and algal concentration. DLM-WQ was calibrated and validated using measured event-based fine particle data from two Lake Tahoe monitoring sites (1999–2010) and estimated external nutrient and fine particle loadings. Secchi depths estimated using the two algorithms with new aggregation rates varied slightly although predictions were very close to each other and to measured Secchi depths. However, Secchi depths and fine particles estimated by the two algorithms with constant aggregation rates deviate largely from those of measured values. Results using measured data and fine particles predicted by the two algorithms showed that fine particles account for approximately 50% of the total light scattering for all sources making it the most significant contributor. Because approximately 70–75% of the scattering is due to fine particles between 0.5 and 8 µm, management efforts should be targeted to control the transport of fine particles from the watershed to the lake. The updated DLM-WQ can be used to simulate lake response in terms of Secchi depth clarity to different fine sediment and nutrients inputs, and inform development of lake management guidelines to facilitate TMDL implementation where water quality is a concern.

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