Some Thoughts About Stochastic Hydrologic Modeling Inspired by the Canadian Wilderness
Vit Klemes, National Hydrology Research Institute, Canada presents Some Thoughts About Stochastic Hydrologic Modeling Inspired by the Canadian Wilderness
| What | Seminar |
|---|---|
| When |
2005-04-28 04:00 PM
2005-04-28 05:00 PM
April 28, 2005 from 04:00 pm to 05:00 pm |
| Where | PES 3001 |
| Contact Name | Vit Klemes |
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Vit Klemes from the National Hydrology Research Institute in Canada will lead a seminar titled Some Thoughts About Stochastic Hydrologic Modeling Inspired by the Canadian Wilderness. All are invited to attend.
Hydrologic science starts with observations of water, continues with recording them, i.e. converting them into “data”, then proceeds to fitting the patterns of these data with mathematical models, and finally uses such models to make predictions about the behavior of water in the frequency and the time domains. It is significant, though often overlooked that, on this route, hydrological science inconspicuously tends to drift ever farther from the “hydro” towards the “logic” with an implicit hope that in doing so it raises its “scientific status”. The irony of this “natural process” is that the most “scientific” predictions about the behavior of the real wet water are often based on the behavior of the rather dry “logical constructs” – mathematical models fitted to pure numbers whose original “hydro” meaning does not enter the picture: the models would be exactly the same regardless of what their underlying numbers might represent. However, what is even more important, is that the main product of these models – their predictions – are usually extrapolations of their “logic” beyond – and often far beyond! – the range of the observations. And it is well known that extrapolation is bad science, except when used as a hypothesis subject to confirmation by observation – a situation seldom if ever encountered in stochastic hydrology. Based on inspirations from the Canadian wilderness (and from other natural settings), the lecture will consider possible ways of “irrigating the dry logic” of stochastic hydrological modeling.