Using an informational entropy-based metric as a diagnostic of flow duration to drive model parameter identification

Global Nest Journal

Volumn 14, Issue 3, 2012, Pages 325-334

  Pechlivanidis, I G ^a   Jackson, B M ^a   McMillan, H K ^b   Gupta, H V ^c  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

^b NATIONAL INSTITUTE OF WATER AND ATMOSPHERIC RESEARCH   (New Zealand)

^c UNIVERSITY OF ARIZONA   (United States)

Author keywords

Calibration; Conditioned entropy difference; Diagnostics; Flow duration curve; Kling gupta efficiency; Model identification; Performance measures

Indexed keywords

EID: 84869466235     PISSN: 17907632     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. Beven K. (2001), Rainfall-Runoff modelling. The Primer, 1-360 pp., John Wiley and Sons, Chichester, UK.
2. Freer J.E., McMillan H., McDonnell J.J. and Beven K.J. (2004), Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures, Journal of Hydrology, 291(3/4), 254-277.
3. Gupta H., Wagener T. and Liu Y. (2008), Reconciling theory with observations: elements of a diagnosticapproach to model evaluation, Hydrological Processes, 22(18), 3802-3813.
4. Gupta H.V., Kling H., Yilmaz K.K. and Martinez G.F. (2009), Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, Journal of Hydrology, 377(1-2), 80-91.
5. Hingray, B., Schaefli, B., Mezghani, A. and Hamdi, Y. (2010), Signature-based model calibration for hydrological prediction in mesoscale Alpine catchments, Hydrological Sciences Journal, 55(6), 1002-1016.
6. Knuth K.H. (2005), Lattice duality: The origin of probability and entropy, Neurocomputing, 67, 245-274.
7. Knuth K.H., Gotera A., Curry C.T., Huyser K.A., Wheeler K.R., Rossow W.B. (2005), Revealing relationships among relevant climate variables with information theory. In Proceedings of the Earth-Sun System Technology Conference (ESTC 2005), July, Adelphi, MD.
8. Koutsoyiannis D. (2005), Uncertainty, entropy, scaling and hydrological statistics. 1. Marginal distributional properties of hydrological processes and state scaling, Hydrological Sciences Journal, 50(3), 381-404.
9. Krause P., Boyle D.P. and Base F. (2005), Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences, 5, 89-97.
10. Moore R.J. (2007), The PDM rainfall-runoff model, Hydrol. Earth Syst. Sci., 11(1), 483-499.
11. Nash J.E. and Sutcliffe J.V. (1970), River flow forecasting through conceptual models, Journal of Hydrology, 10, 282-290.
12. Nichols J.M. (2006), Examining structural dynamics using information flow, Probabilistic Engineering Mechanics, 21(4), 420-433.
13. Papoulis, A. (1991), Probability, Random Variables and Stochastic Processes, 3rd edition, McGraw Hill.
14. Pechlivanidis, I.G., Jackson, B. and McMillan, H. (2010a), The use of entropy as a model diagnostic in rainfall-runoff modelling, iEMSs 2010: International Congress on Environmental Modelling and Software, 5-8 July, Ottawa, Canada, 2, 1780-1787.
15. Pechlivanidis I.G., Jackson B., McIntyre N. and Wheater H.S. (2011), Catchment scale hydrological modelling: A review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications. Global NEST Journal, 13(3)193-214.
16. Pechlivanidis I.G., McIntyre N.R. and Wheater H.S. (2010b), Calibration of the semi-distributed PDM rainfall-runoff model in the Upper Lee catchment, UK, Journal of Hydrology, 386(1-4), 198-209.
17. Ruddell, B.L. and Kumar, P. (2009), Ecohydrologic process networks: 1. Identification, Water Resources Research, 45, W03419.
18. Schaefli B., Harman C.J., Sivapalan M. and Schymanski S.J. (2011), HESS Opinions: Hydrologic predictions in a changing environment: behavioral modeling, Hydrology and Earth System Sciences, 15, 635-646.
19. Schreiber T. (2000), Measuring Information Transfer, Phys. Review Letters, 85(2), 461-464.
20. Shannon C.E. (1948), A mathematical theory of communication, Bell System Technical Journal, 27, 379-423 (Part I) and 623-656 (Part II).
21. Singh V. (2000), The entropy theory as a tool for modelling and decision-making in environmental and water resources, Water S.A., 26(1), 1-12.
22. van Werkhoven K., Wagener T., Reed P. and Tang T. (2008), Characterization of watershed model behavior across a hydroclimatic gradient, Water Resources Research, 44(W01429), 1-16.
23. Weijs, S.V., Schoups, G. and van de Giesen, N. (2010), Why hydrological predictions should be evaluated using information theory, Hydrol.Earth Syst. Sci., 14(12), 2545-2558.
24. Westerberg I.K., Guerrero J.-L., Younger P.M., Beven K.J., Seibert J., Halldin S., Freer J.E. and Xu C.-Y. (2010), Calibration of hydrological models using flow-duration curves, Hydrology and Earth System Sciences, 7(6), 9467-9522.
25. Winsemius H.C., Schaefli B., Montanari A. and Savenije H.H.G. (2009), On the calibration of hydrological models in ungauged basins: A framework for integrating hard and soft hydrological information, Water Resources Research, 45(12), W12422.
26. Woods R. (2004), The impact of spatial scale on spatial variability in hydrologic response: experiments and ideas, Scales in Hydrology and Water Management, 287, 1-15.
27. Yilmaz K.K., Gupta H.V. and Wagener T. (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrological model, Water Resources Research, 44(W09417), 1-18.