A Monte Carlo Approach to Estimation and Prediction

Fedra, K.

doi:10.1007/978-3-642-82054-0_12

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…Spear & Hornberger, 1978;Hornberger & Cosby, 1985) have developed the stochastic method appl/ed here for the testing, sensitivity analysis and calibration of simulation models with a complex structure and sparse data (see also Fedra et al, 1981;Fedra, 1983). The method is based on Monte Carlo simulations, the skeleton of which is to draw a parameter vector p randomly from some a priori distributions, run the model with/; as an input, stpre the result, and repeat the procedure TV times.…”

Section: Analysis Of Performancementioning

confidence: 99%

Performance analysis of a process‐based stand growth model using Monte Carlo techniques

Mäkelä

1988

Scandinavian Journal of Forest Research

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Performance analysis of a process-based stand growth model using Monte Carlo techniques. Accepted Jan. 11, 1988. Scand. J. For. Res. 3:315-331, 1988 The performance of a carbon-balance model of tree growth is analysed, using a generalized sensitivity test based on Monte Carlo techniques. The tree growth model allocates carbon to different biomass compartments according to the principle of functional balance and the pipe-model theory. A simple stand-level version of the model is presented, based on average tree growth and canopy interactions through shading. Plausible ranges of parameter values are estimated from the literature for Scots pine. The results are compared with yield tables for 4 growing sites, and the most critical parameters and assumptions with respect to model validity are identified. The results indicate that a better understanding of the formation of the woody structure of trees is crucial for the further development of the approach. More reliable data are necessary on the parameters related with sapwood respiration and senescence. Secondly, the results are sensitive to light interception, suggesting that the assumption of horizontal homogeneity should be reconsidered, in particular, in the later stage of canopy development. Other important processes with unreliable and sparse data are those related to root functioning.

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Section: Analysis Of Performancementioning

confidence: 99%

Performance analysis of a process‐based stand growth model using Monte Carlo techniques

Mäkelä

1988

Scandinavian Journal of Forest Research

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“…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). referred to "modeling honesty" as the truthful representation of model limitations and uncertainties.…”

Section: Definition Purpose· and Types Of Uncertaintymentioning

confidence: 99%

“…They stated that under no circumstance should an uncertain parameter be held constant simply due to lack of data to define a range or distribution. Fedra (1983) analyzed uncertainty in a lake ecosystem model for modeling a lake's trophic state or water quality. If data were available, the mean and variability were defined.…”

Section: Parameter Distributionsmentioning

confidence: 99%

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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“…To demonstrate the uncalibrated model a simple Monte-Carlo-approach (Fedra, 1983) was used with a Latin Hypercube Sampling (McKay, Conover & Beckman, 1979 ;McKay 1988 ;Iman & Helton, 1988) of the parameters independently from their initial ranges (see Table 2a treshold foodconc . copepods optimal light intensity algae mortality rate zooplankton pmax at 10 °C, no nutrient limitation q10 other algae q10 diatoms q10 zooplankton ingestion qlO zooplankton respiration resp .…”

Section: Uncertainty Analysismentioning

confidence: 99%

SMOES: a simulation model for the Oosterschelde ecosystem

Schölten

Tol²

1994

Hydrobiologia

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SMOES is an invalid model in the Popperian sense of the word . Yet it might be a useful model, which accurately simulates some of the ecosystem behavior . Our knowledge of the Oosterschelde ecosystem is empirical and consists of a comprehensive set of observed data (experiments and in situ measurements) . Model usefulness will be investigated in terms of model adequacy (can a model simulate all system behavior) and reliability (does it simulate observed system behavior only) .The large impact of the building of a storm-surge barrier in the Oosterschelde Mouth (compartment West) allows us to distinguish two different systems . The quality of the simulation model, built and calibrated for the pre-barrier system, can so be tested by using it for the (new) post-barrier system . The question if the model does predict the post-barrier future will be answered .We apply a calibration method which generates not merely a single best value for each of the many badly known parameters, but which also enables us to estimate the uncertainty in the model outcome . If we use this method to calibrate SMOES for the pre-and for the post-barrier situation, and subsequently-estimate the uncertainty in the model results, we find no large (functional) shifts in the behavior of the main ecosystem properties . 453

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A Monte Carlo Approach to Estimation and Prediction

Cited by 14 publications

References 19 publications

Performance analysis of a process‐based stand growth model using Monte Carlo techniques

Performance analysis of a process‐based stand growth model using Monte Carlo techniques

A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY¹

SMOES: a simulation model for the Oosterschelde ecosystem

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A Monte Carlo Approach to Estimation and Prediction

Cited by 14 publications

References 19 publications

Performance analysis of a process‐based stand growth model using Monte Carlo techniques

Performance analysis of a process‐based stand growth model using Monte Carlo techniques

A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY1

SMOES: a simulation model for the Oosterschelde ecosystem

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A WATERSHED‐LEVEL ECOLOGICAL RISK ASSESSMENT METHODOLOGY¹