Trends in BOD/DO Modeling for Wasteload Allocations

Lung, Wu‐Seng

doi:10.1061/(asce)0733-9372(1998)124:10(1004)

Cited by 30 publications

(42 citation statements)

References 1 publication

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“…To compare the simulation results with the observed data is an essential process in model assessment (Lung, 2001). Sequential trials have been performed to modify the hydrodynamics and water quality parameters until the model error was acceptable.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

The potential impact of an inter-basin water transfer project on nutrients (nitrogen and phosphorous) and chlorophyll a of the receiving water system

Zeng

Qin

2015

Science of The Total Environment

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Section: Model Calibration and Validationmentioning

confidence: 99%

The potential impact of an inter-basin water transfer project on nutrients (nitrogen and phosphorous) and chlorophyll a of the receiving water system

Zeng

Qin

2015

Science of The Total Environment

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“…This study applies BASINS to predict hydrologic responses. BASINS is also an integrated decision-making system (Laroche et al 1996;Jacomino and Fields 1997;Whittemore and Beebe 2000;Luzio et al 2002;Albek et al 2004;Hsieh and Yang 2006). Because most environmental data involve spatial variability, many studies use geographic information systems (GISs) (Helmlinger et al 1993;Montgomery and Foufoula-Georgiou 1993;.…”

Section: Basins Modelmentioning

confidence: 99%

“…Runoff prediction can help us understand the risks of floods and droughts. To avoid wasteful and misguided environmental strategies, we must improve the reliability of our models (Lung 2001). The sources of model uncertainty include model structure, observations, model inputs, initial values, and boundary conditions (Troutman 1983;Klepper 1997).…”

Section: Introductionmentioning

confidence: 99%

Effects of spatial data resolution on runoff predictions by the BASINS model

Chang

Chao

2013

Int. J. Environ. Sci. Technol.

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The BASINS model, developed by the United States EPA, is a popular simulation tool for predicting watershed responses, such as runoff, pollution exports, and water quality. It requires large amounts of data to set parameters. Many studies state that model input is a major source of model uncertainty. Thus, improvements to the quality and completeness of the data will improve the certainty of the model. The objective of this study is to discuss the effects of spatial data, including digital elevation models (DEMs) and spatial rainfall records, on predictions of runoff from the BASINS model. The result shows that both DEMs and rainfall data can significantly influence peak flow and runoff volume. Rainfall input has more influence on the curve shape of hydrograph than DEM resolution. DEM resolution can have more impact on peak flow predictions than rainfall input. Because the model uncertainties from DEMs and rainfall records influence each other, the prediction error does not always decrease when DEM resolution increases. The present results show that the BASINS model produces reliable answers in the case area when the grid size is less than 100 m 9 100 m and the precipitation records from the Bihu Rainfall Station are correct and complete.

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“…Models play and will continue to play a central role in the TMDL development and implementation process (Reckhow, 1999;NRC, 2001;Lung, 2001;USEPA, 2002). Whether the models are empirical (statistical) or mechanistic, they represent the best scientific understanding of how contaminant loadings relate to water body impairment of designated beneficial uses (NRC, 2001).…”

Section: Uncertainty In Tmdl Decisionsmentioning

confidence: 99%

A Decision Analysis Approach to TMDL Implementation Decisions: Mercury TMDLS in the San Francisco Bay Area

Labiosa¹,

Leckie²,

Mumley³

et al. 2003

proc water environ fed

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This paper describes a decision analysis approach to TMDL implementation decisions for mercury using a hypothetical mine-impacted tributary in the San Francisco Bay as an example. Decision analysis is a theoretically sound approach for making significant decisions under uncertainty (see, e.g., Howard, 1968;Keeney and Raiffa, 1976;Clemen, 1996;Merkhofer, 1999). The Bayesian probabilistic nature of decision analysis makes it ideal for integrating diverse information, including the results from scientific and engineering models, cost and benefit models, empirical data, and expert judgment. One significant advantage of a decision analysis approach is its explicit separation of a decision problem into alternatives, information, and preferences. This, in theory, allows decision makers and stakeholders to separate "what we know" from "what we want". It is hypothesized that a more explicit separation of information and values/preferences will focus the debate. While traditional decision analysis assumes a single rational decision maker (where "single" may also denote a group that agrees on information and preferences), it can be extended to multiple decision maker situations in a variety of ways. Evaluating various extensions of decision analysis in a TMDL implementation stakeholder context is one of the primary goals of this on-going study. It is hypothesized that, in general, decision analysis provides a helpful decision framework for a TMDL implementation planning/stakeholder process in many circumstances.

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Trends in BOD/DO Modeling for Wasteload Allocations

Cited by 30 publications

References 1 publication

The potential impact of an inter-basin water transfer project on nutrients (nitrogen and phosphorous) and chlorophyll a of the receiving water system

The potential impact of an inter-basin water transfer project on nutrients (nitrogen and phosphorous) and chlorophyll a of the receiving water system

Effects of spatial data resolution on runoff predictions by the BASINS model

A Decision Analysis Approach to TMDL Implementation Decisions: Mercury TMDLS in the San Francisco Bay Area

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