Reduced order emulation of distributed hydraulic simulation models

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 15, Issue PART 1, 2009, Pages 1762-1767

  Young, Peter C ^a,b,c   Leedal, David ^a,d   Beven, Keith J ^a,d   Szczypta, Camille ^e  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

^d UPPSALA UNIVERSITY   (Sweden)

^e Hydrosciences   (France)

Author keywords

Emulation; Estimation; Hydraulic; Hydrological; Identification; Reduced order

Indexed keywords

DOMINANT MODE; DYNAMIC EXPERIMENT; EMULATION; EMULATION MODEL; HEC-RAS MODEL; HYDRAULIC SIMULATION MODEL; HYDROLOGICAL; MONTE CARLO ENSEMBLE; PARAMETER SPACES; REDUCED ORDER; RIVER SEVERN; STATE-DEPENDENT PARAMETERS; TRANSFER FUNCTION MODEL; WATER LEVEL PREDICTION;

DISTRIBUTED COMPUTER SYSTEMS; ESTIMATION; FORECASTING; HYDRAULIC MODELS; HYDRAULIC STRUCTURES; IDENTIFICATION (CONTROL SYSTEMS); UNCERTAINTY ANALYSIS; WATER LEVELS;

COMPUTER SIMULATION;

EID: 80051619663     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20090706-3-FR-2004.0139     Document Type: Conference Paper

References (21)

1. A. D. Al'tshul' and A. M. Kalyakin. Method of determin- ing the normal depth for ow in open channels. Power Technology and Engineering (formerly Hydrotechnical Construction), 10:691-694, 1979.
2. R.L. Barkau, M.C. Johnson, and M.G. Jackson. UNET: a model of unsteady ow through a full network of open channels. In Proceedings of the 1989 National Conference on Hydraulic Engineering, New York, 1989.
3. American Society of Civil Engineers. K. J. Beven. A manifesto for the equifinality thesis. J. Hydrology, 320:18-36, 2006.
4. G. W. Brunner. HEC-RAS, river analysis system hydraulic reference manual. Technical report, U.S. Army Corps of Engineers, Hydrologic Engineering Center, 2001.
5. S. Galelli, F. Pianosi, and R. Soncini-Sessa. Meta-model of an irrigation district distributed-parameter model. In Proceedings, IFAC World Congress, Seoul, Korea., 2008.
6. C. Gu. Smoothing Spline ANOVA Models. Springer- Verlag, 2002.
7. M. J. Lees, P. C. Young, S. Ferguson, K. J. Beven, and J. Burns. An adaptive ood warning scheme for the River Nith at Dumfries. In W. R. White and J. Watts, editors, International Conference on River Flood Hydraulics, pages 65-77. J. Wiley: Chichester, 1994.
8. S.K. Ooi and E. Weyer. Control design of an irrigation channel from physical data. Control Engineering Prac- tice, 16:1132-1150, 2008.
9. F. Pappenberger, K. j. Beven, K. Frodsham, R. Ro- manovicz, and P. Matgen. Grasping the unavoidable subjectivity in calibration of ood inundation models: a vulnerability weighted approach. Journal of Hydrology, 333:275-287, 2006. (Pubitemid 46075123)
10. R. J. Romanowicz, P. C. Young, and K. J. Beven. Data assimilation and adaptive forecasting of water levels in the River Severn catchment. Water Resources Research, 42(W06407):doi:10.1029/2005WR004373, 2006. (Pubitemid 44156556)
11. P. C. Young. Data-based mechanistic modeling of en- vironmental, ecological, economic and engineering systems. Environmental Modelling and Software, 13:105-122, 1998.
12. P. C. Young. Data-based mechanistic modelling, gener- alised sensitivity and dominant mode analysis. Com- puter Physics Communications, 117:113-129, 1999.
13. P. C. Young. The identification and estimation of nonlin- ear stochastic systems. In A. I. Mees, editor, Nonlinear Dynamics and Statistics, pages 127-166. Birkhauser: Boston, 2001.
14. P. C. Young. Advances in real-time ood forecasting. Philosophical Trans. Royal Society, Physical and Engi- neering Sciences, 360(9):1433-1450, 2002.
15. P. C. Young. Top-down and data-based mechanistic modelling of rainfall-ow dynamics at the catchment scale. Hydrological Processes, 17:2195-2217, 2003.
16. P. C. Young and M. Ratto. A unified approach to envi- ronmental systems modeling. Stochastic Environmental Research and Risk Assessment, DOI 10.1007/s00477- 008-0271-1: published on-line, 15 October, 2008.
17. P. C. Young and M. Ratto. From dominant mode anal- ysis to dynamic meta-modelling. In 5th International Conference on Sensitivity of Model Output (SAMO), Budapest, Hungary, June 18-22, 2007.
18. P. C. Young, S. Parkinson, and M. J. Lees. Simplicity out of complexity: Occam's razor revisited. Journal of Applied Statistics, 23:165-210, 1996.
19. P. C. Young, P. McKenna, and J. Bruun. Identification of nonlinear stochastic systems by state dependent param- eter estimation. International Journal of Control, 74: 1837-1857, 2001.
20. P. C. Young, R. J. Romanowicz, and K. J. Beven. Updat- ing algorithms in ood forecasting. Technical Report UR5, Flood Risk Management Research Consortium, www.oodrisk.org.uk, U.K., December 2006.
21. P. C. Young, N. W. Rees, and C. Lu. Simulation model emulation in control system design. Technical Report TR/230, Centre for Research on Environmental Systems and Statistics, Lancaster University, 2009.