The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.

Water Quality Prediction for River Basin Management

Olli Malve

Dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Department of Civil and Environmental Engineering for public examination and debate in Auditorium R2 at Helsinki University of Technology (Espoo, Finland) on the 18th of May, 2007, at 12 noon.

Overview in PDF format (ISBN 978-951-22-8750-5)   [3906 KB]
Dissertation is also available in print (ISBN 978-951-22-8749-9)


Water quality prediction methods are developed which provide realistic estimates of prediction errors and accordingly increase the efficiency of river basin management and the implementation of EU's Water Framework Directive. The resulting river basin management decisions are based on realistic safety margins for restoration measures and accompanying targeted pollutant load limits.

The realistic error estimates attached to the predictions are based on Bayesian statistical inference and MCMC methods which are able to synthesize two distinct water quality prediction approaches i.e. mechanistic and statistical. What is more, a hierarchical modeling strategy is employed in order to pool information from extensive cross-sectional lake monitoring data and consequently to improve the accuracy and precision of lake specific water quality predictions.

Testing of the methods using extensive hydrological and water quality data from five real-world river basin management cases suggests that Bayesian inference and MCMC methods are no more difficult to implement than classical statistical methods. Even models with large numbers of correlated parameters can be fitted using modern computational methods. Moreover, the hierarchical modeling strategy proves to be efficient for river basin management. Guidelines for adaptive river basin management are also set up based on the experience gained. It is proposed that monitoring, prediction and decision making should be integrated into an efficient management procedure.

This thesis consists of an overview and of the following 5 publications:

  1. Malve, O., Huttula, T. and Lehtinen, K. 1991. Modelling of eutrophication and oxygen depletion in the Lake Lappajärvi. In: Wrobel, L., Brebbia, C. (Eds.), Water Pollution: Modelling, Measuring and Prediction. Computational Mechanics Publications, pp. 111-124.
  2. Malve, O., Salo, S., Verta, M. and Forsius, J. 2003. Modeling the transport of PCDD/F compounds in a contaminated river and the possible influence of restoration dredging on calculated fluxes. Environmental Science and Technology, 37 (15), pp. 3413-3421. DOI: 10.1021/es0260723.
  3. Malve, O., Laine, M. and Haario, H. 2005. Estimation of winter respiration rates and prediction of oxygen regime in a lake using Bayesian inference. Ecological Modelling, 182 (2), pp. 183-197. DOI: 10.1016/j.ecolmodel.2004.07.020.
  4. Malve, O., Laine, M., Haario, H., Kirkkala, T. and Sarvala, J. 2006. Bayesian modelling of algal mass occurrences—using adaptive MCMC methods with a lake water quality model. Environmental Modelling and Software, 22 (7), pp. 966-977. DOI: 10.1016/j.envsoft.2006.06.016.
  5. Malve, O. and Qian, S. 2006. Estimating nutrients and chlorophyll a relationships in Finnish lakes. Environmental Science and Technology, 40 (24), pp. 7848-7853. DOI: 10.1021/es061359b.

Keywords: river basin management, target pollutant load, Bayesian inference, MCMC, hierarchical model

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© 2007 Helsinki University of Technology

Last update 2011-05-26