Water quality prediction for river basin management

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.

Department dc:contributor.department

Department of Civil and Environmental Engineering

Grantor dc:publisher

Helsinki University of Technology

Year dc:date.issued

2007

Author dc:creator

• Malve, Olli

Contributors dc:contributor

• Aalto-yliopisto
• Aalto University

Language dc:language.iso

en

Repository record dc:identifier.uri

https://aaltodoc.aalto.fi/handle/123456789/2864

OAI record oai:identifier

https://aaltodoc.aalto.fi/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aaaltodoc.aalto.fi%3A123456789%2F2864