Abstract
Despite the many advances in sensors and recording techniques, monitoring programs can still be relatively expensive. In practice, this often limits the density of monitoring programs. Yet, large amounts of data are monitored and filed without proper analysis of their information contents. The combined use of monitoring and simulation models can reduce the costs and facilitate rigorous analyses of monitored data. Physically based simulation models provide the best means of interpolating between measurement points (in space and time). The models can also aid the effective design of monitoring programs. Field data can be used to improve the quality of simulation models. For real time monitoring, information can be fed back into the simulation models through automatic update routines. These combined techniques, long used for hydraulic data, are now also developed for water quality data. Whenever possible, the integration between monitoring and modeling should be designed from the outset to obtain full benefit. New techniques are developed for linking the two methodologies including data mining, data validation, and data assimilation techniques. The paper describes some of the recent developments in this field, giving examples of practical applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Refsgaard, J.C. (1997) Validation and intercomparison of different updating procedures for real-time forecastingNordic Hydrology28, 65–84.
Madsen, H. and Cañizares, R. (1999) Comparison of extended and ensemble Kalman filter for data assimilation in coastal area modelingInternational Journal for Numerical Methods in Fluids31, 961–981.
Verlaan, M. and Heemink, A.W. (1997) Tidal flow forecasting using reduced rank square root filtersStochastic Hydrol. Hydraul. 11349–368.
Hartnack, J. and Madsen, H. (2001) Data assimilation in river flow modeling, 41’ DHI Software Conference, 6–8 June, 2001, Scanticon Conference Centre, Helsingor, Denmark.
Cañizares, R., Madsen, H., Jensen, H.R., and Vested, H.J. (2001) Developments in operational shelf sea modeling in Danish waters, Estuarine and Coastal Shelf Science (in press).
Madsen, H., Butts, M.B., Khu, S.T., and Liong, S.Y. (2000) Data assimilation in rainfall-runoff forecasting, Proceedings of Hydroinformatics 200041hConference on Hydroinformatics, Cedar Rapids, Iowa, USA, 23–27 July 2000.
Rungo, M., Refsgaard, J.C., and Havno, K. (1991) The updating procedure in the MIKE 11 modeling system for real-time forecasting, in: Proceedings of the International Symposium on Hydrological Applications on Weather Radar, University of Salford, 14–17 August, Ellis Horword, 497–508.
Babovic, V., Keijzer, M., and Bundzel, M. (2000) From global to local modeling: A case study in error correction of deterministic models, Proceedings of Hydroinformatics 2000, 4’5Conference on Hydroinformatics, Cedar Rapids, Iowa, USA, 23–27 July 2000.
Babovic, V., Cañizares, R., Jensen, H.R., and Klinting, A. (2001) Neural networks as routine for error updating of numerical modelsJ. Hydraul. Eng. ASCE127(3), 181–193.
Fanner,.J.D. and Sidorowich, J.J. (1987) Predicting chaotic time seriesPhysical Review Letters59(8), 62–65.
Babovic, V. and Keijzer, M. (1999) Forecasting river discharges in the presence of chaos and noise, in J. Marsalek (ed.)Coping with Floods: Lessons Learned from Recent ExperiencesKluwer, Dordrecht.
Minns, A.W. and Hall, M.J. (1996) Artificial neural networks as rainfall-runoff modelsJournal of Hydrological Sciences41(3), 399–417.
Wan E. (1993) Time Series Prediction by using a connectionist network with internal delays, in A.S. Weigend and N.A. Gershenfeld (eds)Time Series Prediction: Forecasting the Future and Understanding the PastSFI Studies in the Sciences of Complexity, Proc. Vol XV, Addison-Wesley.
Koza, J.R. (1992)Genetic Programming: On the Programming of Computers by Natural SelectionMIT Press, Cambridge.
Babovic, V. and Keijzer, M. (2000) Genetic programming as a model induction engineJournal of Hydroinformatics2(1), 35–60.
WMO (1992)Simulated Real-Time Intercomparison of Hydrological ModelsOperational Hydrology Report no. 38, World Meteorological Organization, Geneva.
Havno, K., Madsen, M.N., and Dorge, J. (1995) MIKE 11 ¡ª a generalized river modelling package, in V.P. Singh (ed.)Computer Models of Watershed HydrologyWater Resources Publications, Colorado, pp. 733–782.
Keijzer, M., and Babovic, V. (1999) Dimensionally aware genetic programming, in W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, and R. E. Smith, (eds.)GECCO-99: Proceedings of the Genetic and Evolutionary Computation ConferenceJuly 13–17, 1999, Orlando, Florida USA. San Francisco, CA: Morgan Kaufmann.
Taghon, G.L, Nowell, A.R.M., and Jumars, P.A. (1984) Transport and breakdown of fecal pellets: biological and sedimentological consequencesLimnol. Oceanogr.29(1), 64–72.
Komar, P.D. and Taghon, G.L. (1985) Analysis of the settling velocities of fecal pellets from the subtidal polychaete amphicteis scaphobronchiateJnl. of Marine Research43(3), 605–614.
Gibbs, R.J., Matthews, M.D., and Link, D.A. (1971) The relationship between sphere size and settling velocityJnl. of Sed. Petrology41(1), 7–18.
Davies, C.N. (1945) Definitive equations for the fluid resistance of spheresProc. of the Physical Society57(4), No. 322.
Rubey, W.W. (1933) Settling velocities of gravel, sand, and silt particlesAmerican Journal of Science25, 325–338.
Hallenneier, R.J. (1981) Terminal settling velocity of commonly occuring sandsSedimentology28, 859–865.
Dietrich, W.E. (1982) Settling velocity of natural particlesWater Resources Research18(6), 1615–1626.
Van Rijn, L.C. (1989)Handbook of Sediment Transport By Currents And WavesDelft Hydraulics, Report H 461.
Babovic, V.. Keijzer, M., Rodriguez, A.D., and Harrington, J. (2001)Automated discovery of settling velocity equationsD2K Technical Report, D2K-0201–1http://d2k.dk/Publications
Babovic, V. (1996)Emergence Evolution Intelligence; HydroinformaticsBalkema, Rotterdam.
Babovic, V. and Abbott, M.B. (1997) The evolution of equations from hydraulic data, Part I: TheoryJournal of Hydraulic Research35(3), 1–14.
Heemink, A., Verlaan, M., and Segers, A.J. (2000) Variance reduced ensemble Kalman filtering, Report 00–03, Department of Applied Mathematical Analysis, Delft University of Technology, The Netherlands.
Kompare, B. (1995) The use of artificial intelligence in ecological modeling, Ph.D. Thesis, University of Copenhagen, Denmark.
Madsen, H. (2000) Automatic calibration of a conceptual rainfall-runoff model using multiple objectivesJournal of Hydrology(accepted).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Havno, K., Madsen, H., Babovic, V. (2003). Integrated Use of Monitoring and Modeling in Water Resources Research. In: Harmancioglu, N.B., Ozkul, S.D., Fistikoglu, O., Geerders, P. (eds) Integrated Technologies for Environmental Monitoring and Information Production. Nato Science Series, vol 23. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0231-8_24
Download citation
DOI: https://doi.org/10.1007/978-94-010-0231-8_24
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-1399-7
Online ISBN: 978-94-010-0231-8
eBook Packages: Springer Book Archive