Bayesian networks in environmental applications – experience from lake and river management
Olli Varis

Water Resources Laboratory, 
Helsinki University of Technology, 
P.O. Box 5200, 02015 HUT, Finland
E-mail: olli.varis@tkk.fi
Tel. +358-9-451 3844, fax +358-9-451 3856

Abstract 
To know something is a positive thing. The more one knows, the better. Though, the knowledge should not be overly one-sided. The associative ability to connect issues with one another, detecting analogies and controversies is the more important the more complex issues one is dealing with. 
Let us play with the idea of constructing a model for the future development of water resources on a large, international river basin in deepest jungles of Asia or harshest deserts of Africa, given the myriad interconnections of water to environment, poverty, urbanization, food production, and other things that we only can imagine. We were conscious about the underlying importance of the concept of sustainability, and understood, that it means we should leave those regions to our kids in a shape that they do not have much reason to blame us. Then, after one generation they would do the same to their kids, we hope. 
We would select a few dozen most significant things that are under change—such as population growth, urbanization, climate, economy, human resources, technology, and industry—and then tried to define the ones impacted by these changes: from ecology, social issues, economy, and so on. This passivity would start to bother us soon; we would brainstorm how things could be driven to the direction we want, to make the world as good as possible after a few decades. Such policies need policy makers and institutions and stakeholders. 
Now, it would be most interesting to scrutinize all the various interconnections of these issues. Naturally, they all can be—and to a certain level are—interconnected. To make this without losing the track in processing so many interconnections at the same time, we need a systematic way to do it. We should analyze each link systematically—whether it is negative or positive, whether it can be ignored, is small or big? 
How should such diverse and uncertain things be presented in a way, which could be used to each of the issues in the same way? Perhaps a good idea would be to set absolute numbers partly aside, and emphasize things as relative to one another, and relative as well as to the state of any of the issues in some specific time slot, as we consider the time to go on in our exercise, years ahead from now. The present situation could be a good comparison: whether the issue will grow, decrease, stay unchanged. Very few of us could, though, say anything with certainty, but prefer to say on a certain item that it is likely to grow, but there are reasonable chances that it does not change at all, or even goes down. We could take probability distributions to describe all this. 
Now that we have wrote down all we know about the issues and their interconnections, it would be most rewarding to see, whether we were consistent at all, what are the most critical issues with respect to some other issues, what if we changed something, how the other issues would react, or even what we should say to ministries to do to outline better policies. 
Examples from Mekong River in Asia and Senegal River in Africa are summarized. Bayesian network models were developed and used to address the challenges outlined above. The cases belong to large-scale consultations to governments and international river basin authorities in those regions.
Related Web links
Author’s home page: 
	Olli Varis: http://www.water.hut.fi/wr/research/glob/egloshow/home_varis.html
Project Web pages:
Water and Development Project: http://www.water.hut.fi/wr/research/globaali.en.html
WUP-FIN Mekong Modelling Project: http://www.eia.fi/WUP-FIN/wup-fin1/index.html