Whatever the case, one of the best talk I attended to at last Fall American Geophysical Union Meeting, was the invited lecture by Hoshin Gupta. Hoshin has an outstanding (really outstanding, I mean) carrier in finding calibration methods, indentifiability of parameters and understanding uncertainty in models. Recently (see for instance Gong et al., 2013) he started to apply concepts derived from information theory to hydrology. BTW, you can find the pdfs of his AGU’s presentations here: on the necessity to apply information theory concept to evaluate models structural hypotheses, and another one about Information theory and Bayesian inference in hydrology (both with a lot of citations).

I never really understood why hydrologists do not use information theory concepts. I-Theory is a well developed mathematica theory with a lot of tools, and could help to get out from the fuzziness around the uncertainty. Besides, using the concept of I-Theory information/uncertainty one can gain knowledge about the complexity of processes outputs and, possibly, infer something about the "complexity" of models required to mathematically account for them (remind: "Everything should be made as simple as possible but not simpler").

Hoshin is not the only one that was attracted by information theory. In my occasional browsing of the topic, I also found at least a couple of other interesting papers: the first one, by Majda and Gershgorin, is concerned by climate models. This is encouraging, because climate models are certainly at least as involved as hydrological models are, and, if not, even more. A second is Weijs et al. (2013): this is concerned with time series: we compare time series, therefore knowing how much information is hidden in a time serie (at least with reference according to some encoding key) is certainly useful. For Wejis and van de Giesen, this paper is just a coming back to the topic (see also Weijs et al., 2010, and Weijs CV)

Another paper came from Rudell on EOS remarkably highlighting that the I-Theory applications in hydrology attracted last year many more people than use to be. For making me feeling among the smarter, I bought a book, by Mezard (see also, and GS) and Montanari (Andrea, not our colleague Alberto who also has quite a production on uncertainty: please see his website) which can be a further source of ideas and thoughts.

So far, I never actually read carefully any one of the papers (or the book), but excited at the idea to have time to do it in deep.

References

Gong, W., H. V. Gupta, D. Yang, K. Sricharan, and A. O. Hero III (2013), Estimating epistemic and aleatory uncertainties during hydrologic modeling: An information theoretic approach, Water Resour. Res., 49, 2253–2273, doi:10.1002/wrcr.20161.

Mézard, M. and Montanari, A. , Information, Physics, and Computation, Oxford University press, 2009

Majda, A. J., and Gershgorin, B., Quantifying uncertainty in climate change science through empirical information theory, PNAS, August 24, 2010, vol. 107,no. 34, 14958–14963

Ruddel, B.L, N. A. Brunsell and P. C. Stoy, Applying Information Theory in the Geosciences to Quantify Process Uncertainty, Feedback, Scale, Eos, Vol. 94, No. 5, 29 January 2013

Weijs, S. V.; Schoups, G. and van de Giesen, N., Why hydrological predictions should be evaluated using information theory, Hydrol. Earth Syst. Sci., 14, 2545-2558, 2010, www.hydrol-earth-syst-sci.net/14/2545/2010/, doi:10.5194/hess-14-2545-2010

Weijs, S. V., van de Giesen, N. and Parlange, M. B., Data compression to define information content of hydrological time series, Hydrol. Earth Syst. Sci., 17, 3171–3187, 2013 www.hydrol-earth-syst-sci.net/17/3171/2013/ doi:10.5194/hess-17-3171-2013