From pointwise testing to a regional vision: An integrated statistical approach to detect nonstationarity in extreme daily rainfall. Application to the Sahelian region
Global warming is expected to intensify the hydrologic cycle. Documenting whether significant changes in the extreme precipitation regimes have already happened is consequently one of the challenging topics in climatic research. The high natural variability of extreme precipitation often prevents from obtaining significant results when testing changes in the empirical distribution of extreme rainfall at regional scale. A regional integrated approach is proposed here as one possible answer to this complex methodological problem. Three methods are combined in order to detect regionally significant trends and/or breakpoints in series of annual maximum daily rainfall: (1) individual stationarity tests applied to the raw point series of maxima, (2) a maximum likelihood testing of time-dependent generalized extreme value (GEV) distributions fitted to these series, and (3) a heuristic testing of a regional time-dependent GEV distribution. This approach is applied to a set of 126 daily rain gauges covering the Sahel over the period 1950-1990. It is found that only a few stations are tested as nonstationary when applying classical tests on the raw series, while the two GEV-based models converge to show that the extreme rainfall series indeed underwent a negative breakpoint around 1970. The study evidences the limits of the widely used classical stationarity tests to detect trends in noisy series affected by sampling uncertainties, while using a parametric space and time-dependent GEV efficiently reduces this effect. Showing that the great Sahelian drought was accompanied by a significant decrease of extreme rainfall events is the other main result of this study.