This paper highlights the usefulness of the minimum information and parametric pair-copula construction (PCC) to model the joint distribution of flood event properties. Both of these models outperform other standard multivariate copula in modeling multivariate flood data that exhibiting complex patterns of dependence, particularly in the tails. In particular, the minimum information pair-copula model shows greater flexibility and produces better approximation of the joint probability density and corresponding measures have capability for effective hazard assessments. The study demonstrates that any multivariate density can be approximated to any degree of desired precision using minimum information pair-copula model and can be practically used for probabilistic flood hazard assessment.
Bibliographical noteOpen Access funded by Natural Environment Research Council
Under a Creative Commons license.
Copyright © and Moral Rights are retained by the author(s) and/ or other
copyright owners. A copy can be downloaded for personal non-commercial
research or study, without prior permission or charge. This item cannot be
reproduced or quoted extensively from without first obtaining permission in
writing from the copyright holder(s). The content must not be changed in any way
or sold commercially in any format or medium without the formal permission of
the copyright holders.
- Flood frequency analysis
- Flood hazard characterization
- Return period
- D-vine model
- Minimum information pair-copula model
- Himalaya (India)
Daneshkhah, A., Remesan, R., Chatrabgoun, O., & Holman, I. (2016). Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model. Journal of Hydrology, 540, 469-487. https://doi.org/10.1016/j.jhydrol.2016.06.044