A method for quantifying the prediction uncertainties associated with water quality models

Summers, James K.; Wilson, Harold T.; Kou, Jingyee

doi:10.1016/0304-3800(93)90078-7

Cited by 27 publications

(23 citation statements)

References 8 publications

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“…Unlike sensitivity analysis which considers the sensitivity of the model output to slight changes in an input parameter or variable, uncertainty analysis considers the inherent uncertainty in model input data and the subsequent effect this uncertainty has on model prediction. A number of uncertainty methods have been developed and applied specifically to hydrologic and water resource problems (Dettinger & Wilson, 1981;Beck, 1987;Schanz & Salhotra, 1992;Summers et al, 1993). Recently, Loague et al (1989 and Zhang et al (1993a) used uncertainty analysis specifically for deterministic transport models in the vadose zone.…”

Section: Uncertaintymentioning

confidence: 99%

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Corwin

2015

SSSA Special Publications

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In recent years, worldwide attention has shifted from point source to non-point source (NPS) pollutants, particularly with regard to the pollution of surface and subsurface sources of drinking water. This is due to the widespread occurrence and potential chronic health effects of NPS pollutants. The ubiquitous nature of NPS pollutants poses a complex technical problem. The area1 extent of their contamination increases the complexity and sheer volume of data required for assessment far beyond that of typical point source pollutants. The spatial nature of the NPS pollution problem necessitates the use of a geographic information system (GIS) to manipulate, retrieve, and display the large volumes of spatial data. This chapter provides an overview of the components (i.e., spatial variability, scale dependency, parameter-data estimation and measurement, uncertainty analysis, and others) required to successfully model NPS pollutants with GIS and a review of recent applications of GIS to the modeling of non-point source pollutants in the vadose zone with deterministic solute transport models. The compatibility, strengths, and weaknesses of coupling a GIS to deterministic one-dimensional transport models are discussed. BACKGROUND IN NON-POINT SOURCE POLLUTANTS

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Section: Uncertaintymentioning

confidence: 99%

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Corwin

2015

SSSA Special Publications

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“…Although progress has been made in the areas of uncertainty analysis (e.g., Dale et al 1988;Gardner and O'Neill 1981;Gertner and Guan 1991;Gertner et al 1995;Hanes et al 1991;Kremer 1981;McCarthy et al 1995;O'Neill and Gardner 1979;O'Neill et al 1980;Rossing et al 1994a,b;Summers et al 1993) and error budgets (Gelb et al 1974;Gertner and Guan 1991;Gertner et al 1995), it is necessary to develop the statistical and computational tools that will enable model users to jointly assess and quantify the sources and magnitude of input error, develop error budgets, and optimize data collection, modeling and simulation, and management decisions in terms of errors, expense and risks for the array of large scale simulation models employed in resource assessment and management.…”

Section: Models and Error Budgetsmentioning

confidence: 99%

Error and Uncertainty Analysis for Ecological Modeling and Simulation

Gertner¹

2001

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“…Uncertainty in spatial data has been greeted by a conspiracy of silence and few, if any, existing pollutant transport and fate models include thorough uncertainty analyses Reckhow, 1994). Model uncertainty and error analysis are major, but poorly understood aspects of risk assessment and modeling (Beck, 1987;Summers et al, 1993). We must learn to live with uncertainty and incorporate it into numerical analysis and modeling, rather than ignore it (Fedra, 1983).…”

Section: Definition Purpose· and Types Of Uncertaintymentioning

confidence: 99%

“…concluded that Monte Carlo simulation is the ~· .,, ... , most robust method for propagating uncertainty···fur~ugh either simple or complex models. Therefore, given the limitations of first-order analysis, Monte Carlo procedures are the preferred method of propagating uncertainty in complex, watershed-level hydrologic and water quality (H/WQ) models Summers et al, 1993;Taskinen et al, 1994;Haan and Zhang, 1995;Prabhu, 1995). 10.…”

Section: Propagation Of Uncertaintymentioning

confidence: 99%

“…There are two main categories of methods for estimating the uncertainty in model predictions: first-order variance propagation and Monte Carlo simulation methods (Beck, 1987;Summers et al, 1993;Zhang et al, 1993). First-order variance techniques have a number of theoretical shortcomings that reduce their utility (Summers et al, 1993).…”

Section: Propagation Of Uncertaintymentioning

confidence: 99%

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A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY¹

Hession¹,

Storm²,

Haan³

et al. 1996

J American Water Resour Assoc

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: We present an ecological risk assessment methodology at the watershed level for freshwater ecosystems. The major component is a pollutant transport and fate model (a modified EUTROMOD) with an integrated uncertainty analysis utilizing a two‐phase Monte Carlo procedure. The uncertainty analysis methodology distinguishes between knowledge uncertainty and stochastic variability. The model assesses the ecological risk of lentic (lake) ecosystems in response to the stress of excess phosphorus resulting in eutrophication. The methodology and model were tested on the Wister Lake watershed in Oklahoma with the lake and its trophic state as the endpoint for ecological risk assessment. A geographic information system was used to store, manage, and manipulate spatially referenced data for model input.

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A method for quantifying the prediction uncertainties associated with water quality models

Cited by 27 publications

References 8 publications

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Error and Uncertainty Analysis for Ecological Modeling and Simulation

A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY¹

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A method for quantifying the prediction uncertainties associated with water quality models

Cited by 27 publications

References 8 publications

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Error and Uncertainty Analysis for Ecological Modeling and Simulation

A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY1

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A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY¹