Predictability and Nonlinear Modeling in Natural Sciences and Economics

By: G. Van Straten (Editor), James G. van Straten (Editor), J. Grasman (Editor)

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Researchers in the natural sciences are faced with problems that require a novel approach to improve the quality of forecasts of processes that are sensitive to environmental conditions. Nonlinearity of a system may significantly complicate the predictability of future states: a small variation of parameters can dramatically change the dynamics, while sensitive dependence of the initial state may severely limit the predictability horizon. Uncertainties also play a role. This volume addresses such problems by using tools from chaos theory and systems theory, adapted for the analysis of problems in the environmental sciences. Sensitive dependence on the initial state (chaos) and the parameters are analyzed using methods such as Lyapunov exponents and Monte Carlo simulation. Uncertainty in the structure and the values of parameters of a model is studied in relation to processes that depend on the environmental conditions. These methods also apply to biology and economics. For research workers at universities and (semi)governmental institutes for the environment, agriculture, ecology, meteorology and water management, and theoretical economists.

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Introduction	p. 1
Karl Popper and the accountability of scientific models	p. 6
Evaluation of forecasts	p. 11
The Liouville equation and prediction of forecast skill	p. 29
An improved formula to describe error growth in meteorological models	p. 45
Searching for periodic motions in long-time series	p. 57
Comparison study of the response of the climate system to major volcanic eruptions and el nino events	p. 65
Detection of a pertubed equator-pole temperature gradient in a spectral model of the atmospheric circulation	p. 86
A simple two-dimensional climate model with ocean and atmosphere coupling	p. 95
Climate modelling at different scales of space	p. 113
Simulation of effects of climatic change on cauliflower production	p. 127
Validation of large scale process-oriented models for managing natural resource populations: a case study	p. 138
Uncertainty of predictions in supervised pest control in winter wheat, its price and its causes	p. 149
The implications and importance of non-linear responses in modelling the growth and development of wheat	p. 157
Growth curve analysis of sedentary plant parasitic nematodes in relation to plant resistance and tolerance	p. 172
Using chaos to understand biological dynamics	p. 184
Qualitative analysis of unpredictability: a case study from childhood epidemics	p. 204
Control and prediction in seasonally driven population models	p. 216
Simple theoretical models and population predictions	p. 228
Individual based population modelling	p. 232
Ecological systems are not dynamical systems: some consequences of individual variability	p. 248
Spatio-temporal organization mediated by hierarchy in time scales in ensembles of predator-prey pairs	p. 260
Continental expansion of plant disease: a survey of some recent results	p. 274
Modelling of fish behavior	p. 282
Understanding uncertain environmental systems	p. 294
System identification by approximate realization	p. 312
Sensitivity analysis versus uncertainty analysis: when to use what?	p. 322
Monte Carlo estimation of uncertainty contributions from several independent multivariate sources	p. 334
Assessing sensitivities and uncertainties in models: a critical evaluation	p. 344
UNCSAM: a software tool for sensitivity and uncertainty analysis of mathematical models	p. 362
Set-membership identification of nonlinear conceptual models	p. 377
Parameter sensitivity and the quality of model predictions	p. 389
Towards a metrics for simulation model validation	p. 398
Use of a Fourier decomposition technique in aquatic ecosystems modelling	p. 411
Multiobjective inverse problems with ecological and economical motivations	p. 422
An expert-opinion approach to the prediction problem in complex systems	p. 432
Critical loads and a dynamic assessment of ecosystem recovery	p. 439
Uncertainty analysis on critical loads for forest soils in Finland	p. 447
Monte Carlo simulations in ecological risk assessment	p. 460
Sensitivity analysis of a model for pesticide leaching and accumulation	p. 471
Bayesian uncertainty analysis in water quality modelling	p. 485
Modelling dynamics of air pollution dispersion in mesoscale	p. 495
Uncertainty factors analysis in linear water quality models	p. 505
Uncertainty analysis and risk assessment combined: application to a bioaccumulation model	p. 516
Diagnosis of model applicability by identification of incompatible data sets illustrated on a pharmacokinetic model for dioxins in mamals	p. 527
Regional calibration of a steady state model to assess critical acid loads	p. 541
Uncertainty analysis for the computation of greenhouse gas concentrations in IMAGE	p. 554
Forecast uncertainty in economics	p. 568
Some aspects of nonlinear discrete-time descriptor systems in economics	p. 581
Quasi-periodic and strange, chaotic attractors in Hick's nonlinear trade cycle model	p. 591
Monte Carlo experimentation for large scale forward-looking economic models	p. 599
Erratic dynamics in a restricted tatonnement process with two and three goods	p. 609
Chaotic dynamics in a two-dimensional overlapping generation model: a numerical investigation	p. 621
Nonlinearity and forecasting aspects of periodically integrated autoregressions	p. 633
Classical and modified rescaled range analysis: some evidence	p. 638
Subject index	p. 648
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Predictability and Nonlinear Modeling in Natural Sciences and Economics

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