A Unified Statistical Framework for Detecting Trends in Multi-Timescale Precipitation Extremes: Application to Non-Stationary Intensity-Duration-Frequency Curves

Abstract:

There is a large agreement that global warming induces changes of precipitation regimes of different nature and amplitude depending on the timescale considered. This question is of special concern regarding extreme rainfall that might have critical socio-environmental consequences. A unified framework is proposed here for detecting trends in extreme rainfall. It is based on the GEV distribution, whose parameters depend both on a simple scaling formulation and on time to account for the non-stationarity deriving from climatic trends. The implementation of the model is illustrated by analysing a set of multi-temporal rainfall data recorded in the Sahelian region of West Africa. While the separate analysis of each of the 30 point series proves inconclusive when it comes to detecting trends at any of the time-steps considered, the combination of a regional and of a simple time-scaling approaches highlighted the existence of a significant trend (p-value < 1%). This trend essentially appears in the scale parameter of the regional GEV distribution, involving a 15 to 20% increase of the 10-year rainfall in 30 years, and a 23 to 30% increase of the 100-year rainfall. The main advantages of the proposed framework are i) its parsimony, allowing for reducing the uncertainty associated with the model inference, ii) its capacity for detecting trends either in the mean and/or in the variability of the extreme events and iii) its ability for producing coherent non-stationary Intensity-Duration-Curves over a range of event durations.