Citation:

Riasi, S., A. Teklitz, W. Shuster, C. Nietch, AND L. Yeghiazarian. Reliability-Based Water Quality Assessment with Load Resistance Factor Design: Application to TMDL. Journal of Hydrologic Engineering . American Society of Civil Engineers (ASCE), Reston, VA, 23(12):1943-5584, (2018). https://doi.org/10.1061/(ASCE)HE.1943-5584.0001722

Impact/Purpose:

Effective load reduction strategies rely on an accurate Total Maximum Daily Load (TMDL) calculation, which quantifies contaminant loading from various sources. There is a wide range of methods to consider uncertainties in TMDLs: from simple, conservative assumptions regarding factors that contribute to the TMDL required margin of safety (MOS), to probability-based approaches such as Monte Carlo simulations, which explicitly quantifies TMDL uncertainty. In this paper the authors adapt the Load Resistance Factor Design (LRFD), a rigorous, reliability-based framework, to water quality assessment and the TMDL process.

Description:

Effective load reduction strategies rely on an accurate Total Maximum Daily Load (TMDL) calculation, which quantifies contaminant loading from various sources. There is a wide range of methods to consider uncertainties in TMDLs: from simple, conservative assumptions regarding factors that contribute to the TMDL required margin of safety (MOS), to probability-based approaches such as Monte Carlo simulations, which explicitly quantifies TMDL uncertainty. In this paper the authors adapt the Load Resistance Factor Design (LRFD), a rigorous, reliability-based framework, to water quality assessment and the TMDL process. The LFRFD replaces the lumped MOS with design factors that reflect the magnitude and distribution of uncertainty among the various contaminant loads. In addition, it produces load reduction estimates to meet management objectives with a contaminant-specific frequency-based target. The LRFD is computationally efficient and flexible in that, to compute the design factors, the procedure can utilize: measurement data, analytical solutions or model simulation results, as well as full or marginal probability distributions.

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