Data-based mechanistic modelling, generalised sensitivity and dominant mode analysis

Since the inherent uncertainty associated with most environmental and climatic systems is often acknowledged, it is surprising that most mathematical models of such systems are large, complex and completely deterministic in nature. In this situation, it seems sensible to consider alternative modelling methodologies which overtly acknowledge the often poorly defined nature of such systems and attempt to find simpler, stochastic descriptions which are more appropriate to the often limited data and information base. This paper considers one such approach, Data-based Mechanistic (DBM) modelling, and demonstrates how it can be useful not only for the modelling of environmental and other systems directly from time series data, but also as an approach to the evaluation and simplification of large deterministic simulation models. To achieve these objectives, the DBM approach exploits various methodological tools, including advanced methods of statistical identification and estimation; a particular form of Generalised Sensitivity Analysis based on Monte Carlo Simulation; and Dominant Mode Analysis, the latter involving a new statistical approach to combined model linearisation and order reduction. These various techniques are outlined in the paper and they are applied to the stochastic modelling of water pollution in rivers and the evaluation of nonlinear global carbon cycle models.

[1]  T. Wigley,et al.  Implications for climate and sea level of revised IPCC emissions scenarios , 1992, Nature.

[2]  K. Hasselmann Climate-change research after Kyoto , 1997, Nature.

[3]  Karel J. Keesman,et al.  Set Membership Approach to Identification and Prediction of Lake Eutrophication , 1990 .

[4]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[5]  P. Young,et al.  Recent advances in the data-based modelling and analysis of hydrological systems , 1997 .

[6]  A. Jakeman,et al.  Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments , 1990 .

[7]  M. B. Beck,et al.  Uncertainty and arbitrariness in ecosystems modelling: A lake modelling example , 1981 .

[8]  A. W. Kemp,et al.  Statistics for the Environment. , 1993 .

[9]  Fred C. Schweppe,et al.  Evaluation of likelihood functions for Gaussian signals , 1965, IEEE Trans. Inf. Theory.

[10]  Gill Begnor Concise encyclopaedia of environmental systems: Edited by Peter C. Young. Pergamon Press Ltd. 769 pp. ISBN: 0-08-036198-6 , 1994 .

[11]  B. Bayus,et al.  Dynamic Modelling and Control of National Economies , 1982 .

[12]  John A. Taylor Fossil fuel emissions required to achieve atmospheric CO2 stabilisation using ANU-BACE: A box-diffusion carbon cycle model , 1996 .

[13]  Peter C. Young,et al.  Time-variable parameter and trend estimation in non-stationary economic time series , 1994 .

[14]  M. B. Beck,et al.  A dynamic model for DO—BOD relationships in a non-tidal stream , 1975 .

[15]  R. Huggett,et al.  Modelling the Human Impact on Nature: Systems Analysis of Environmental Problems , 1993 .

[16]  H. Oeschger,et al.  A box diffusion model to study the carbon dioxide exchange in nature , 1975 .

[17]  Peter C. Young,et al.  Identification and system parameter estimation , 1985 .

[18]  Peter C. Young,et al.  A non-minimal state variable feedback approach to multivariable control of glasshouse climate , 1995 .

[19]  Peter C. Young,et al.  Recursive Estimation and Time Series Analysis , 1984 .

[20]  Peter C. Young,et al.  Uncertainty and sensitivity in global carbon cycle modelling , 1998 .

[21]  Keith Beven,et al.  On the sensitivity of soil-vegetation-atmosphere transfer (SVAT) schemes: equifinality and the problem of robust calibration , 1997 .

[22]  R. C. Spear,et al.  The application of Kolmogorov–Rényi statistics to problems of parameter uncertainty in systems design† , 1970 .

[23]  C. Praagman,et al.  System Dynamics in Economic and Financial Models , 1997 .

[24]  Peter C. Young,et al.  Data-based mechanistic modelling of environmental, ecological, economic and engineering systems. , 1998 .

[25]  J. Houghton Climate change 1994 : radiative forcing of climate change and an evaluation of the IPCC IS92 emission scenarios , 1995 .

[26]  Peter C. Young,et al.  Data-based mechanistic modelling and the rainfall-flow non-linearity. , 1994 .

[27]  P. Young,et al.  Proportional-integral-plus (PIP) design for delta (delta) operator systems Part 2. MIMO systems , 1998 .

[28]  R. Spear Eutrophication in peel inlet—II. Identification of critical uncertainties via generalized sensitivity analysis , 1980 .

[29]  Peter C. Young,et al.  What can tracer profiles tell us about the mechanisms giving rise to them , 1996 .

[30]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[31]  M. B. Beck,et al.  Uncertainty and forecasting of water quality , 1983 .

[32]  Peter C. Young,et al.  Water quality in river systems: Monte‐Carlo Analysis , 1979 .