Environmental Management

, Volume 59, Issue 4, pp 584–593 | Cite as

Probabilistic Evaluation of Ecological and Economic Objectives of River Basin Management Reveals a Potential Flaw in the Goal Setting of the EU Water Framework Directive

  • Turo Hjerppe
  • Antti Taskinen
  • Niina Kotamäki
  • Olli Malve
  • Juhani Kettunen


The biological status of European lakes has not improved as expected despite up-to-date legislation and ecological standards. As a result, the realism of objectives and the attainment of related ecological standards are under doubt. This paper gets to the bottom of a river basin management plan of a eutrophic lake in Finland and presents the ecological and economic impacts of environmental and societal drivers and planned management measures. For these purposes, we performed a Monte Carlo simulation of a diffuse nutrient load, lake water quality and cost-benefit models. Simulations were integrated into a Bayesian influence diagram that revealed the basic uncertainties. It turned out that the attainment of good ecological status as qualified in the Water Framework Directive of the European Union is unlikely within given socio–economic constraints. Therefore, management objectives and ecological and economic standards need to be reassessed and reset to provide a realistic goal setting for management. More effort should be put into the evaluation of the total monetary benefits and on the monitoring of lake phosphorus balances to reduce the uncertainties, and the resulting margin of safety and costs and risks of planned management measures.


Water framework directive Bayes network Modelling Uncertainty Monte Carlo simulation 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no competing interests.


  1. Aguilera PA, Fernández A, Fernández R, Rumí R, Salmerón A (2011) Bayesian networks in environmental modelling. Environ Model Softw 26:1376–1388CrossRefGoogle Scholar
  2. Barton DN, Kuikka S, Varis O, Uusitalo L, Henriksen HJ, Borsuk M, de la Hera A, Farmani R, Johnson S, Linnell JDC (2012) Bayesian networks in environmental and resource management. Integr Environ Assess Manag 8(3):418–429CrossRefGoogle Scholar
  3. Barton DN, Saloranta T, Moe SJ, Eggestad HO, Kuikka S (2008) Bayesian belief networks as a meta-modelling tool in integrated river basin management—pros and cons in evaluating nutrient abatement decisions under uncertainty in a Norwegian river basin. Ecol Econom 66:91–104CrossRefGoogle Scholar
  4. Borowski I, Hare M (2007) Exploring the gap between water managers and researchers: difficulties of model-based tools to support practical water management. Water Res Mgmt 21:1049–1074CrossRefGoogle Scholar
  5. Brugnach M, Tagg A, Keil F, de Lange WJ (2007) Uncertainty matters: computer models at the science-policy interface. Water Res Mgmt 21:1075–1090CrossRefGoogle Scholar
  6. Carmona G, Varela-Ortega C, Bromley J (2013a) Supporting decision making under uncertainty: Development of a participatory integrated model for water management in the middle Guadiana river basin. Environ Model Softw 50:144–157CrossRefGoogle Scholar
  7. Carmona G, Varela-Ortega C, Bromley J (2013b) Participatory modeling to support decision making in water management under uncertainty: Two comparative case studies in the Guadiana river basin, Spain. J Environ Manage 128:400–412CrossRefGoogle Scholar
  8. Carter JG, White I (2012) Environmental planning and management in an age of uncertainty: the case of the water framework directive. J Environ Manage 113:228–236CrossRefGoogle Scholar
  9. Chen SH, Pollino CA (2012) Good practice in Bayesian network modelling. Environ Model Softw 37:134–145CrossRefGoogle Scholar
  10. Duarte CM, Conley DJ, Carstensen J, Sanchez-Camacho M (2009) Return to neverland: shifting baselines affect eutrophication restoration targets. Estuaries Coasts 32:29–36. doi: 10.1007/s12237-008-9111-2 CrossRefGoogle Scholar
  11. Hering D, Borja A, Carstensen J, Carvalho L, Elliott M, Feld CK, Heiskanen A-S, Johnson RK, Moe J, Pont D, Lyche Solheim A, van de Bund W (2010) The European water framework directive at the age of 10: a critical review of the achievements with recommendations for the future. Sci Total Environ 408:4007–4019CrossRefGoogle Scholar
  12. Hjerppe T, Väisänen S (2015) A practical tool for selecting cost-effective combinations of phosphorus loading mitigation measures in Finnish catchments. Int J River Basin Manage 13:363–376. doi: 10.1080/15715124.2015.1012516 CrossRefGoogle Scholar
  13. Huttunen I, Huttunen M, Piirainen V, Korppoo M, Lepistö A, Räike A, Tattari S, Vehviläinen B (2016) A national scale nutrient loading model for Finnish watersheds — VEMALA. Environ Model Assess 21:83–109CrossRefGoogle Scholar
  14. Jensen FV (2001) Bayesian networks and decision graphs. Statistics for Engineering and Information Science. Springer, New York, p 263CrossRefGoogle Scholar
  15. Jeppesen E, Søndergaard M, Jensen JP, Havens K, Anneville O, Carvalho L et al. (2005) Lake responses to reduced nutrient loading—an analysis of contemporary long-term data from 35 case studies. Freshw Biol 50:1747–1771CrossRefGoogle Scholar
  16. Kjærulff UB & Madsen AL (2005). Probabilistic networks—An introduction to Bayesian networks and influence diagrams. [online] Accessed 25 Mar 2015
  17. Kotamäki N, Pätynen A, Taskinen A, Huttula T and Malve O (2015). Statistical dimensioning of nutrient loading reduction—LLR assessment tool for lake managers. Environ Manage. doi: 10.1007/s00267-015-0514-0.
  18. Laine M (2008). Adaptive MCMC methods with applications in environmental and geophysical models. Dissertation, Lappeenranta University of Technology.Google Scholar
  19. Lehikoinen A (2014). Bayesian network applications for environmental risk assessment. Doctoral dissertation. University of Helsinki, Department of Environmental Sciences. p 41.Google Scholar
  20. Malve O (2007). Water quality prediction for river basin management. Doctoral dissertation. Helsinki University of Technology. Espoo, Finland. TKK-DISS-2292. ISBN 978-951-22-8749-9.
  21. Malve O, Hjerppe T, Tattari S, Väisänen S, Huttunen I, Kotamäki N, Kallio K, Taskinen A, Kauppila P (2016) Participatory operations model for cost-efficient monitoring and modeling of river basins — A systematic approach. Sci Total Environ 540:79–89. doi: 10.1016/j.scitotenv.2015.06.105 CrossRefGoogle Scholar
  22. Malve O, Laine M, Haario H (2005) Estimation of winter respiration rates and prediction of oxygen regime in a lake using Bayesian inference. Ecol Modell 182(2):183–197. doi: 10.1016/j.ecolmodel.2004.07.020 CrossRefGoogle Scholar
  23. Malve O, Laine M, Haario H, Kirkkala T, Sarvala J (2006) Bayesian modelling of algal mass occurrences—using adaptive MCMC methods with a lake water quality model. Environ Model Softw 22(7):966–977. doi: 10.1016/j.envsoft.2006.06.016 CrossRefGoogle Scholar
  24. Malve O, Qian S (2006) Estimating nutrients and chlorophyll a relationships in Finnish Lakes. Environ Sci Technol 40(24):7848–7853. doi: 10.1021/es061359b CrossRefGoogle Scholar
  25. Molina J-L, García-Aróstegui JL, Bromley J, Benavente J (2011) Integrated assessment of the European WFD implementation in extremely overexploited aquifers through participatory modelling. Water Res Mgmt 25:3343–3370CrossRefGoogle Scholar
  26. Raadgever GT, Dieperink C, Driessen PPJ, Smit AAH, van Rijswick HFMW (2011) Uncertainty management strategies: lessons learnt from the regional implementation of the water framework directive in the Netherlands. Environ Sci Policy 14:64–75CrossRefGoogle Scholar
  27. Refsgaard JC, van der Sluijs JP, Højberg AL, Vanrolleghem PA (2005) Uncertainty in the environmental modelling process—A framework and guidance. Environ Model Softw 22:1543–1556CrossRefGoogle Scholar
  28. Reichert P, Borsuk ME (2005) Does high forecast uncertainty preclude effective decision support? Environ Model Softw 20:991–1001CrossRefGoogle Scholar
  29. Saloranta TM, Kämäri J, Rekolainen S, Malve O (2003) Benchmark criteria: a tool for selecting appropriate models in the field of water management. Environ Manage 32(3):322–333CrossRefGoogle Scholar
  30. Uusitalo L (2007) Advantages and challenges of Bayesian networks in environmental modelling. Ecol Modell 203:312–318CrossRefGoogle Scholar
  31. Varis O, Kettunen J, Sirviö H (1990) Bayesian influence diagram approach to complex environmental management including observational design. Comput Stat Data Anal 9:77–91CrossRefGoogle Scholar
  32. WATECO (2003). Economics and the Environment: The Implementation Challenge of the Water Framework Directive [online]. EU Working Group guideline for WFD implementation. 270 p. Available from: Accessed 5 Apr 2013
  33. Zorrilla P, Carmona G, De la Hera Á, Varela-Ortega C, Martínez-Santos P, Bromley J, Jorgen Henriksen H (2009) Evaluation of Bayesian networks as a tool for participatory water resources management: application to the upper Guadiana basin in Spain. Ecol Soc 15(3):12CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Finnish Environment Institute, PL140HelsinkiFinland
  2. 2.Finnish Environment Institute, PL35JyväskyläFinland

Personalised recommendations