Understanding Uncertain Environmental Systems

Beck, M.B.

doi:10.1007/978-94-011-0962-8_25

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…Precisely how one goes about this task of corroborating and refuting the constituent hypotheses of the model is now widely agreed, in philosophical terms, to have been amply and adequately stated in Popper's papers on the scientific method and the evolution of knowledge (Popper, 1959(Popper, , 1963as illustrated by Caswell, 1976;Young, 1978;Re&how and Chapra, 1983;Konikow and Bredehoeft, 1992;Ababou et al, 1992;and Beck, 1994). This does not imply, however, that.…”

Section: I Caswell's Contributionmentioning

confidence: 94%

On the problem of model validation for predictive exposure assessments

Beck

Ravetz

Mulkey

et al. 1997

Stochastic Hydrol Hydraul

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The development and use of models for predicting exposures are increasingly common and are essential for many risk assessments of the United States Environmental Protection Agency (EPA). Exposure assessments conducted by the EPA to assist regulatory or policy decisions are often challenged to demonstrate their "scientific validity". Model validation has thus inevitably become a major concern of both EPA officiMs and the regulated community, sufficiently so that the EPA's Risk Assessment Forum is considering guidance for model validation. The present paper seeks to codify the issues and extensive foregoing discussion of validation with special reference to the development and use of models for predicting the impact of novel chemicals on the environment. Its preparation has been part of the process in formulating a White Paper for the EPA's Risk Assessment Forum. Its subject matter has been drawn from a variety of fields, including ecosystem analysis, surface water quality management, the contamination of groundwaters from high-level nuclear waste, and the control of air quality. The philosophical and conceptual bases of model validation are reviewed, from which it is apparent that validation should be understood as a task of product (or tool) design, for which some form of protocol for quality assurance will ultimately be needed. The commonly used procedures and methods of model validation are also reviewed, including the analysis of uncertainty. Following a survey of pe~st attempts at resolving the issue of modeI validation, we close by introducing the notion of a model having maximum relevance to the performance of a specific task, such as, for example, a predictive exposure assessment.Key words. Model validation; analysis of uncertainty; model verification; quality assurance; system identification; model calibration. I n t r o d u c t i o nThe construction and use of m a t h e m a t i c a l models are essential in predicting the possible consequences of releasing chemicals, some of which m a y be quite novel, into new environments. Substantial costs, and substantial damages to the environment, m a y attach to the regulatory decisions that are informed and thus guided by the predictions derived from a model. T h e risk of making a wrong decision will be strongly dependent on the reliability of these predictions, in just the same way as it

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Section: I Caswell's Contributionmentioning

confidence: 94%

On the problem of model validation for predictive exposure assessments

Beck

Ravetz

Mulkey

et al. 1997

Stochastic Hydrol Hydraul

Self Cite

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“…Yet, since the analysis is only limited to the identification stage, further study of uncertainty analysis for the validation stage is essential to confirm and verify our conclusions. Then, for a given lysimeter it should be possible to select the most appropriate model from the tested candidate models that pass the validation test by using a systematic model discrimination procedure [Russo, 1988;Beck, 1994].…”

Section: Discussionmentioning

confidence: 99%

Identification and uncertainty analysis of soil water retention models using lysimeter data

Chen¹,

Wheater²

1999

Water Resources Research

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The water retention characteristic is one of the fundamental properties that control water movement in unsaturated soils. Because of the complexity and heterogeneity of soil properties, variability and uncertainty of this property has long been a key issue in understanding the relationship between water content and matric pressure. Although considerable progress has been achieved in understanding spatial and local variability, discussions have primarily been based on deterministic analyses. Misleading conclusions can result since the derived variability cannot often be discriminated from uncertainties in model parameters, structure, and observed data. Using a combined deterministic and stochastic analysis, this paper addresses the variability of the retention characteristic in association with the uncertainty of water retention models. Calibrated against two sets of lysimeter data observed over 2 years, the analysis yields insights into the physical changes in the system, and model performance is significantly improved when these effects are explicitly included. sessment of model structures and uncertainties becomes essential in model identification. Although considerable research has been devoted to the development of various water retention models, estimation of spatial variability, derivation of model parameters from soil texture [McCuen et al., 1981], and interpretation of the physical meaning of the empirical parameters, issues of model uncertainty and identifiability have not been simultaneously and properly discussed. Consequently, misleading conclusions can easily result, in particular, when the model is applied to a range of states that is not covered by calibration data. For instance, when the model is applied deterministically to a soil condition for which it is not calibrated, unidentifiable parameters may become sensitive. Model structural uncertainty can be misinterpreted as spatial variability in a heterogeneous geological medium when large-scale heterogeneity is derived from sample scale data [Desbarats, 1995]. Milly [1987] showed that multiple minima exist in the objective functions used to estimate optimally the parameters of water retention models, a clear indication of lack of parameter identifiability. Thus uncertainty can pose both practical and more fundamental problems in the application of water retention models. Currently, calibration of water retention models is mostly carried out with data from drainage experiments, either through laboratory analysis of field samples, for which a relatively wide range of data is available [Ferrand and Celia, 1992], or in situ by simultaneous measurement of soil water tension and volumetric water content [Hopmans, 1987]. Although model parameters can be better identified from laboratory data, the results are often quite different from measurements at a field site [Wierenga et al., 1991; Eching et al., 1994] because of the differences in the conditions under which the model is calibrated (in the laboratory) and the model is applied (in the field). Howeve...

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“…Model development requires the support of data analysis, interpretation, hypothesis testing and the reconciliation of model concepts with field observation (Beck 1994). The distillation of hypothesis is essential to model formulation and may help eliminate unsound preconceptions more swiftly than might otherwise be the case (Wheater and Beck 1995;Eder et al 2003).…”

Section: Structural Uncertaintymentioning

confidence: 99%