Uncertainty and arbitrariness in ecosystems modelling: A lake modelling example

Fedra, K.; Straten, G. van; Beck, M.B.

doi:10.1016/0304-3800(81)90008-9

Cited by 77 publications

(30 citation statements)

References 11 publications

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“…The general pattern of the results emerging from the use of Monte Carlo simulation, at least simply in terms of the magnitudes of the prediction errors propagated, is not substantially different from that of the first-order error analyses [e.g., Burges and Lettenmaier, 1975;Hornberger, 1980 ;O'Neill et al, 1980;Gardner et al, 1980aGardner et al, , 1981aGardner and O'Neill, 1983;Scavia et al, l98lb;Fedra et al, 1981 ;Walker, 1982;Somlyody, 1983].…”

Section: (T) Bp(t) B"(t) and B~(t) Having Elements [Ox;(t)/oxit mentioning

confidence: 99%

“…It may not be necessary to burden the manager with the details of an analysis of error propagation. But most certainly it is important to be concerned about the scientific basis underpinning the model and its predictions, and this is becoming acutely important given the impressive and persuasive technology of communication now within reach Fedra and Loucks, 1985]. At the same time, it is surprisingly difficult to convince the scientist, let alone the manager, that system identification and the analysis and interpretation of field data are integral to the development of scientific theories about the behavior of complex environmental systems Beck, 1982Beck, , 1985b.…”

Section: The Issuesmentioning

confidence: 99%

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Water quality modeling: A review of the analysis of uncertainty

Beck¹

1987

Water Resources Research

871

431

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This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. More specifically, four problem areas are examined in detail: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model. The review is rather lengthy, and it has therefore been prepared in effect as two papers. There is a shorter, largely nontechnical version, which gives a quick impression of the current and future issues in the analysis of uncertainty in water quality modeling. Enclosed by this shorter discussion is the main body of the review dealing in turn with (1) identifiability and experimental design, (2) the generation of preliminary model hypotheses under conditions of sparse, grossly uncertain field data, (3) the selection and evaluation of model structure, (4) parameter estimation (model calibration), (5) checks and balances on the identified model, i.e., model “verification” and model discrimination, and (6) prediction error propagation. Much time is spent in discussing the algorithms of system identification, in particular, the methods of recursive estimation, and in relating these algorithms and the subject of identification to the problems of prediction uncertainty and first‐order error analysis. There are two obvious omissions from the review. It is not concerned primarily with either the development and solution of stochastic differential equations or the issue of decision making under uncertainty, although clearly some reference must be made to these topics. In brief, the review concludes (not surprisingly) that much work has been done on the analysis of uncertainty in the development of mathematical models of water quality, and much remains to be done. A lack of model identifiability has been an outstanding difficulty in the interpretation and explanation of past observed system behavior, and there is ample evidence to show that the “larger,” more “comprehensive” models are easily capable of generating highly uncertain predictions of future behavior. For the future of the subject, it is speculated that there is the possibility of progress in the development of novel algorithms for model structure identification, a need for new questions to be posed in the problem of prediction, and a distinct challenge to the conventional views of this review in the new forms of knowledge representation and manipulation now emerging from the field of artificial intelligence.

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Section: (T) Bp(t) B"(t) and B~(t) Having Elements [Ox;(t)/oxit mentioning

confidence: 99%

Section: The Issuesmentioning

confidence: 99%

Water quality modeling: A review of the analysis of uncertainty

Beck¹

1987

Water Resources Research

871

431

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“…Related analyses have been conducted in water quality modeling (see, e.g., Burges and Littenmaier, 1975;Fedra et al, 1981 or Beck and van Straten, 1983) and in modeling other ecological systems (e.g. Gardner et al,, 1980).…”

Section: Introductionmentioning

confidence: 99%

A framework for error analysis of a long-range transport model with emphasis on parameter uncertainty

Alcamo

Bartnicki

1987

Atmospheric Environment (1967)

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“…The analysis as applied here is more concerned with parameter estimation than testing the model structure (e.g. Fedra et al 1981). Therefore, it remains to be tested how well the model performance will fit biomass data under low nitrogen availability.…”

Section: Discussionmentioning

confidence: 99%

A process-based growth model for the grass stage pine seedlings

Koskela¹

2000

Silva Fenn.

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A carbon-and nitrogen-balance model, applying pipe model theory and a modification of functional balance as growth-guiding rules, is presented for the grass stage pine seedlings. Three populations of Pinus merkusii Jungh. et de Vriese, originating from northern and northeastern Thailand, were grown under controlled environment for 47 weeks to obtain parameter information, to evaluate the model performance and to investigate genotypic variation in various characteristics among the populations. Monte Carlo simulations were used to evaluate the sensitivity of the model behaviour to varying parameter values and to calibrate the model for each population.With given sets of parameter values, the simulated biomass development fitted rather well the observed one during the experiment. The two most important parameters determining model performance were within-shoot shading and specific nitrogen uptake rate of fine roots. The fit of simulated versus measured fine roots had a major effect on acceptable model performance in Monte Carlo simulations. Significant variation in biomass growth, nitrogen use efficiency, height, stem diameter, total carbon concentrations of stem and fine roots, and total nitrogen concentrations of needles, transport roots and fine roots was found among the populations. The observed genotypic variation in seedling biomass and stem diameter was consistent with the geographical distribution of the populations while the variation in the rest of the measured characteristics was not. It seems that P. merkusii populations in Thailand are adapted to more site specific conditions rather than climatic conditions alone, and that the variation in biomass growth may result from variation in internal carbon and nitrogen dynamics among the populations.

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Uncertainty and arbitrariness in ecosystems modelling: A lake modelling example

Cited by 77 publications

References 11 publications

Water quality modeling: A review of the analysis of uncertainty

Water quality modeling: A review of the analysis of uncertainty

A framework for error analysis of a long-range transport model with emphasis on parameter uncertainty

A process-based growth model for the grass stage pine seedlings

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