Recent advances in the data-based modelling and analysis of hydrological systems

Water Science and Technology

Volumn 36, Issue 5, 1997, Pages 99-116

  Young, Peter C ^a   Jakeman, Anthony J ^b   Post, David A ^c  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c OREGON STATE UNIVERSITY   (United States)

Author keywords

Dynamic harmonic regression (DHR); Econometrics; Hydrological systems; Signal extraction; Unobserved components (UC)

Indexed keywords

FEATURE EXTRACTION; MATHEMATICAL MODELS; REGRESSION ANALYSIS; TIME SERIES ANALYSIS;

DYNAMIC HARMONIC REGRESSION (DHR); UNOBSERVED COMPONENTS (UC) MODEL;

HYDROLOGY;

HYDROLOGICAL MODELLING; STATISTICAL ANALYSIS; TIME SERIES;

CONFERENCE PAPER; HYDRODYNAMICS; HYPOTHESIS; MODEL; STATISTICAL ANALYSIS; WATER FLOW; WATER TRANSPORT;

EID: 0030721451     PISSN: 02731223     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0273-1223(97)00465-4     Document Type: Conference Paper

References (26)

1. Akaike, H. (1974). A new look at statistical model identification. IEEE Trans. Auto. Control, AC-19: 716-723.
2. Bryson, A. E. and Ho, Y. C. (1969). Applied Optimal Control. Blaisdell, Waltham, Mass.
3. Harvey, A. C. (1989). Forecasting Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge.
4. Jakeman, A.J. and Hornberger, G.M. (1993). How much complexity is warranted in a rainfall-runoff model? Wat. Resour. Res., 29(8): 2637-2649.
5. Jakeman, A. J. and Young, P. C. (1979). Refined instrumental variable methods of recursive time-series analysis, Part 2: multivariable systems. Int. Jnl. Control, 29: 621-624.
6. Jakeman, A. J. and Young, P. C. (1984). Recursive filtering and the inversion of ill-posed causal problems. Utilitas Mathematica, 35: 351-376 (appeared originally as Report No. AS/R28/1979, Centre for Resource and Environmental Studies, Australian National University, 1979).
7. Jakeman, A. J., Littlewood, I. G. and Whitehead, P. G. (1990). Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology, 117: 275-300.
8. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. ASME Trans., Jnl. Basic Eng, 83-D: 95-108.
9. Kraijenhoff, D. A. and Moll, J.R. (1986). River Flow Modelling and Forecasting (Water Science and Technology Library), D. Reidel, Dordrecht: 40.
10. Ng, C. N. and Young, P. C. (1990). Recursive estimation and forecasting of nonstationary time series. Journal of Forecasting, 9: 173-204.
11. Norton, J. P. (1986). An Introduction to Identification. Academic Press, London.
12. Post, D. A. and Jakeman, A. J. (1996). Relationships between catchment attributes and hydrological response characteristics in small Australian mountain ash catchments. Hydrol. Proc. 10: 877-892.
13. Young, P.C. (1984). Recursive Estimation and Time-Series Analysis. Springer-Verlag, Berlin.
14. Young, P.C. (1985). The instrumental variable method: a practical approach to identification and system parameter estimation. In Identification and System Parameter Estimation 1985, Vols. 1 and 2, eds. H.A. Barker and P.C. Young, Pergamon, Oxford: 1-16.
15. Young, P. C. (1988). Recursive extrapolation, interpolation and smoothing of nonstationary time-series. Identification and System Parameter Estimation, ed. H.F. Chen, Pergamon, Oxford: 33-44.
16. Young, P.C. (1992a). Parallel processes in hydrology and water quality: a unified time series approach. Jnl. Inst. of Water and Environmental Management, 6: 598-612.
17. Young, P. C. (1992b). Parallel processes in hydrology and water quality: objective inference from hydrological data. Chapter 2 in Water Quality Modelling, ed. R.A. Falconer, Ashgate, Vermont: 10-52.
18. Young, P. C. (1993). Time variable and state dependent modelling of nonstationary and nonlinear time series. Chapter 26 in Developments in Time Series Analysis, ed. T. Subba Rao, Chapman and Hall, London: 374-413.
19. Young, P. C. (1994). Time variable parameter and trend estimation in nonstationary economic time series, Journal of Forecasting, 13 (special issue on 'Time Series Advances in Economic Forecasting'): 179-210.
20. Young, P. C. and Beven, K. J. (1994). Data-Based Mechanistic (DBM) modelling and the rainfall-flow nonlinearity. Environmetrics, 5 (special issue on 'Environmental Time Series Analysis'): 335-363.
21. Young, P. C. and Jakeman, A. J. (1979). Refined instrumental variable methods of recursive time-series analysis, Part 1: single input, single output systems. Int. Jnl. Control, 29: 1-30.
22. Young, P. C. and Jakeman, A. J. (1980). Refined instrumental variable methods of recursive time-series analysis, Part 3: extensions. Int. Jnl. Control, 31: 741-764.
23. Young, P. C. and Ng, C. N. (1989). Variance intervention. Jnl. of Forecasting, 8: 399-416.
24. Young, P. C., Ng, C. N. and Armitage, P. (1989). A systems approach to economic forecasting and seasonal adjustment. International Journal on Computers and Mathematics with Applications, 18 (special Issue on 'System Theoretic Methods in Economic Modelling'): 481-501.
25. Young, P. C., Tych, W. and Pedregal, D. J. (1997) Dynamic Harmonic Regression (DHR) and seasonal adjustment: a spectral approach (in preparation).
26. Young, P. C., Ng, C. N., Lane, K. and Parker, D. (1991). Recursive forecasting, smoothing and seasonal adjustment of nonstationary environmental data, Jnl. of Forecasting, 10: 57-89.