Assessment of Probability Distribution of Dissolved Oxygen Deficit

Tung, Yeou‐Koung; Hathhorn, Wade E.

doi:10.1061/(asce)0733-9372(1988)114:6(1421)

Cited by 28 publications

(10 citation statements)

References 17 publications

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“…Ever since, simulation methods and first-order-second-moment methods have found application in a variety of studies primarily focusing on the evaluation of parameter uncertainty related to the dissolved oxygen concentration and biochemical oxygen demand (e.g. [2,3,10,15,16,19,27]). Jaffe and Ferrara [13] and Yulianti et al [32] also applied uncertainty analysis to model the behavior of contaminated sediments in the water column.…”

Section: Introductionmentioning

confidence: 99%

Assessment of uncertainty sources in water quality modeling in the Niagara River

Franceschini

Tsai

2010

Advances in Water Resources

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Section: Introductionmentioning

confidence: 99%

Assessment of uncertainty sources in water quality modeling in the Niagara River

Franceschini

Tsai

2010

Advances in Water Resources

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“…For example, Tung [1990] compared the performance of MFOSM, FORM, and MCS for evaluating the probability of violating various dissolved oxygen (DO) standards for a hypothetical case study. A similar study was carried out by Melching and Anmangandla [1992], who used the hypothetical DO case studies of Burges and Lettenmaier [1975] and Tung and Hathhorn [1988]. In both papers, the performance of FORM was very similar to that of MCS.…”

Section: First-order Reliability Methods (Form)mentioning

confidence: 70%

First‐order reliability method for estimating reliability, vulnerability, and resilience

Maier¹,

Lence²,

Tolson³

et al. 2001

Water Resources Research

123

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Abstract. Reliability, vulnerability, and resilience provide measures of the frequency, magnitude, and duration of the failure of water resources systems, respectively. Traditionally, these measures have been estimated using simulation. However, this can be computationally intensive, particularly when complex system-response models are used, when many estimates of the performance measures are required, and when persistence among the data needs to be taken into account. In this paper, an efficient method for estimating reliability, vulnerability, and resilience, which i s based on the First-Order Reliability Method (FORM), is developed and demonstrated for the case study of managing water quality in the Willamette River, Oregon. Reliability, vulnerability, and resilience are determined for different dissolved oxygen (DO) standards. DO is simulated using a QUAL2EU water quality response model that has recently been developed for the Oregon Department of Environmental Quality (ODEQ) as part of the Willamette River Basin Water Quality Study (WRBWQS). The results obtained indicate that FORM can be used to efficiently estimate reliability, vulnerability, and resilience. IntroductionThe risk-based performance measures reliability, vulnerability, and resilience were first introduced to the water resources community by Heshimoto et el. computational inefficiency. This becomes especially important if estimates of reliability, resilience, and vulnerability are used to optimize decisions, as many estimates of these criteria may be required, depending on the optimization algorithm used.In this paper, an approach using the First-Order Reliability Method (FORM) is developed as an alternative to simulation for obtaining probabilistic estimates of reliability, vulnerability, and resilience. The approach is illustrated for an example water quality case study based on the Willamette River, Oregon. The remainder of the paper is organized as follows. In section 2, details of FORM are given, and the relative advantages and disadvantages of the method are discussed. In section 3, the approach that uses FORM to estimate reliability, vulnerability, and resilience is outlined, and th e case study is introduced in section 4. The results of the case study are presented and discussed in section 5, and conclusions are given in section 6. Reliability Analysis IntroductionThe performance of any engineered system can be expressed in terms of its load (demand) and resistance (capacity). Use of the load resistance analogy for water resources problems has been discussed by a number of authors, including Duckstein end Bernier [1986] and Kundzewicz [1989]. For example, in the water supply case, water demand corresponds to system load and supply capacity to system resistance, whereas in the water quality case, pollution load and a given water quality standard correspond to the system's load and resistance, respectively.

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“…Although most of the parameters have a COV of less than 15%, the BOD decay rate and biological oxidation of ammonia to nitrate have a COV of 25% and the reaeration rate coefficient presents a COV of 50% (Brown and Barnwell 1987;Bowie et al 1985;House and Skavroneck 1981). Tung and Hathhorn (1988) and Burges and Lettenmaier (1975) present two different sets of statistical properties of model parameters that can be applicable to the Streeter and Phelps (1925) dissolved oxygen equation. The COV of the atmospheric reaeration coefficient (K a ) and the deoxygenation coefficient (K d ) are similar with a value of 30%.…”

Section: Literature Reviewmentioning

confidence: 99%

Random Monte Carlo simulation analysis and risk assessment for ammonia concentrations in wastewater effluent disposal

Carrasco

Chang

2005

Stoch Environ Res Ris Assess

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High concentrations of ammonia in a river can cause fish kills and harms to other aquatic organisms. A simple water quality model is needed to predict the probability of ammonia concentration violations as compared to the US Environmental Protection Agency's ammonia criteria. A spreadsheet with Random Monte Carlo (RMC) simulations to model ammonia concentrations at the mixing point (between a river and the effluent of a wastewater treatment plant) was developed with the use of Microsoft Excel and Crystal Ball add-in software. The model uses effluent and river ammonia, alkalinity, and total carbonate data to determine the probability density functions (PDFs) for the Monte Carlo simulations. Normal, lognormal, exponential and uniform probability distributions were tested using the Chi-square method and p-value associated with it to choose the best fit to the random data selected from the East Burlington wastewater treatment plant in North Carolina and the Clinch River in Tennessee. It is suggested that different options be tested with a minimum of three classes and a maximum of n/5 classes (n = number of data points) and the highest probability (pvalue) for the PDF being tested be chosen. The results indicted that six violations to the EPA criterion for maximum concentration (CMC) were predicted when using 2000 RMC simulations and PDFs fitted to the available data, which violate the current criterion of no more than one violation over 3 years. All violations occur when the pH of the blend ranges from 8.0 to 9.0. No violations were found to the criteria of chronic concentration (CCC) using RMC.

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Assessment of Probability Distribution of Dissolved Oxygen Deficit

Cited by 28 publications

References 17 publications

Assessment of uncertainty sources in water quality modeling in the Niagara River

Assessment of uncertainty sources in water quality modeling in the Niagara River

First‐order reliability method for estimating reliability, vulnerability, and resilience

Random Monte Carlo simulation analysis and risk assessment for ammonia concentrations in wastewater effluent disposal

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