Dealing with Non-stationarity in Intensity-Frequency-Duration Curve
Abstract
Extremes like flood and drought are becoming frequent and more vulnerable in recent times, generally attributed to the recent revelation of climate change. One of the main concerns is that whether the present infrastructures like dams, storm water drainage networks, etc., which were designed following the so called `stationary' assumption, are capable of withstanding the expected severe extremes. Stationary assumption considers that extremes are not changing with respect to time. However, recent studies proved that climate change has altered the climate extremes both temporally and spatially. Traditionally, the observed non-stationary in the extreme precipitation is incorporated in the extreme value distributions in terms of changing parameters. Nevertheless, this raises a question which parameter needs to be changed, i.e. location or scale or shape, since either one or more of these parameters vary at a given location. Hence, this study aims to detect the changing parameters to reduce the complexity involved in the development of non-stationary IDF curve and to provide the uncertainty bound of estimated return level using Bayesian Differential Evolutionary Monte Carlo (DE-MC) algorithm. Firstly, the extreme precipitation series is extracted using Peak Over Threshold. Then, the time varying parameter(s) is(are) detected for the extracted series using Generalized Additive Models for Location Scale and Shape (GAMLSS). Then, the IDF curve is constructed using Generalized Pareto Distribution incorporating non-stationarity only if the parameter(s) is(are) changing with respect to time, otherwise IDF curve will follow stationary assumption. Finally, the posterior probability intervals of estimated return revel are computed through Bayesian DE-MC approach and the non-stationary based IDF curve is compared with the stationary based IDF curve. The results of this study emphasize that the time varying parameters also change spatially and the IDF curves should incorporate non-stationarity only if there is change in the parameters, though there may be significant change in the extreme rainfall series. Our results evoke the importance of updating the infrastructure design strategies for the changing climate, by adopting the non-stationary based IDF curves.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.H22B..01R
- Keywords:
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- 1817 Extreme events;
- HYDROLOGY;
- 1821 Floods;
- HYDROLOGY;
- 4321 Climate impact;
- NATURAL HAZARDS;
- 4343 Preparedness and planning;
- NATURAL HAZARDS