Observed increase in extreme daily rainfall in the French Mediterranean

Abstract

We examine long-term trends in the historical record of extreme precipitation events occurring over the French Mediterranean area. Extreme events are considered in terms of their intensity, frequency, extent and precipitated volume. Changes in intensity are analysed via an original statistical approach where the annual maximum rainfall amounts observed at each measurement station are aggregated into a univariate time-series according to their dependence. The mean intensity increase is significant and estimated at + 22% (+ 7 to + 39% at the 90% confidence level) over the 1961–2015 period. Given the observed warming over the considered area, this increase is consistent with a rate of about one to three times that implied by the Clausius–Clapeyron relationship. Changes in frequency and other spatial features are investigated through a Generalised Linear Model. Changes in frequency for events exceeding high thresholds (about 200 mm in 1 day) are found to be significant, typically near a doubling of the frequency, but with large uncertainties in this change ratio. The area affected by severe events and the water volume precipitated during those events also exhibit significant trends, with an increase by a factor of about 4 for a 200 mm threshold, again with large uncertainties. All diagnoses consistently point toward an intensification of the most extreme events over the last decades. We argue that it is difficult to explain the diagnosed trends without invoking the human influence on climate.

Appendices

Appendix 1: Treatment of missing data

This Appendix describes the treatment applied to missing data. In brief, we ensure that only a very small amount of data is missing; otherwise the station is discarded. Allowing a small amount of missing data is very useful, however, as the number of valid stations would otherwise have been substantially decreased. The required conditions are the following:

• a year is considered complete if more than 335 daily measurements are available, and

• a station is discarded if more than 3 years are missing.

Consequently, a few stations involved in the analysis exhibit missing data. In these cases, gaps at location s are filled using the following imputation method:

• we use stations with complete coverage over the 1961–2015 period (about two thirds of the total number of stations) to compute a reference time-series of a regional mean RX1d, say $r_t$,

• from the RX1d values at location s, say $x_t$, we compute the temporal mean $\overline{x}$ over all available years at this location,

• we compute the temporal mean $\overline{r}$ of the reference time-series $r_t$ over the same years,

• the reconstructed value of year i at location s is derived assuming that year i is as unusual at location s as it is on the reference time-series r, i.e.

  $$\begin{aligned} x_i = r_i \ \frac{\overline{x}}{\overline{r}}. \end{aligned}$$

Appendix 2: On-line scripts

The main scripts used in this paper are made available on the CNRM website and the first author’s webpage at the following URL: http://www.umr-cnrm.fr/spip.php?article23&lang=en. The scripts are written in R, which is a free software environment for statistical computing and graphics. The scripts provided enable replicating both the intensity and frequency analyses. We also provide synthetic data which can be used to run the codes.