Riasi M Sadegh, Teklitz Allen, Shuster William, Nietch Christopher, Yeghiazarian Lilit
Graduate Student, Department of Chemical and Environmental Engineering, University of Cincinnati, OH, USA.
Research Hydrologist, United States Environmental Protection Agency, Cincinnati, OH, USA.
J Hydrol Eng. 2018;23(12):1943-5584. doi: 10.1061/(ASCE)HE.1943-5584.0001722.
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.
有效的负荷削减策略依赖于精确的每日最大总负荷(TMDL)计算,该计算可量化来自各种来源的污染物负荷。有多种方法可用于考虑TMDL中的不确定性:从对构成TMDL所需安全边际(MOS)的因素进行简单、保守的假设,到基于概率的方法,如蒙特卡罗模拟,后者可明确量化TMDL的不确定性。在本文中,作者将荷载抗力系数设计(LRFD)(一种严格的、基于可靠性的框架)应用于水质评估和TMDL过程。LFRFD用反映各种污染物负荷中不确定性的大小和分布的设计因素取代了集中的MOS。此外,它还能根据特定污染物的基于频率的目标,生成负荷削减估计值以满足管理目标。LRFD在计算上高效且灵活,因为在计算设计因素时,该程序可以利用:测量数据、解析解或模型模拟结果,以及完整或边际概率分布。