A1170
Title: Enhancing intensity-duration-frequency curves estimation: A Markov dependence approach
Authors: Mehwish Zaman - University of Padova (Italy)
Isadora Antoniano-Villalobos - Ca Foscari University of Venice (Italy) [presenting]
Ilaria Prosdocimi - Ca Foscari University of Venice (Italy)
Abstract: A fundamental challenge in hydrological risk assessment is the accurate estimation of the probabilities of rainfall events exceeding given (typically high) intensities. Since the design of infrastructures must take into account rainfall extremes at different temporal scales, coherence between estimated exceedance probabilities across different durations is desirable. Intensity duration frequency (IDF) curves, which describe the expected frequency of extreme rainfall intensities measured at different durations, are an important tool in this context. Most existing methods for IDF estimation introduce adequate shape constraints through duration-dependent generalized extreme value (dGEV) distributions but assume that rainfall accumulations are independent across durations. While this assumption does not seem realistic, the introduction of dependence in existing models comes at a high computational cost and often requires approximate inference. An alternative model is proposed based on a first-order Markov assumption, which incorporates dependence between consecutive (equally spaced) rainfall durations via bivariate generalized extreme value (GEV) distributions, while the marginal distributions are dGEV, satisfying shape constraints. The flexibility of the model depends on the family of bivariate distributions controlling the dependence, which can be parametric or nonparametric.
