Evidence of non‐stationarity in a local climatology of rainfall extremes in northern Italy. (5th July 2017) - Uboldi, Francesco; Lussana, Cristian
ABSTRACT: The stationarity of rainfall annual maxima statistics is investigated in a northern Italian area characterized by important spatial variations in orography and precipitation climatology. Climatic changes in Italian recorded temperature series are widely acknowledged. In 2016, though, there is still discussion on the stationarity of the statistics of precipitation and its extremes in Italy. By means of standard hypothesis testing techniques, it is shown, for the first time in Italy, that changes in rainfall annual maxima climatology are indeed present in the period 1950–2005, relevant with regard to global climate change. In particular, in the rainy mountainous northwestern part of Lombardy, adjacent to the Swiss and Piedmont border, the annual maxima distribution for 6 to 24‐h event durations significantly moves towards higher values when going from the first half (1950–1977) to the second half (1978–2005) of the period. It is remarkable, moreover, that for 1‐h duration the distribution significantly moves towards lower values, suggesting a possible change in the precipitation regime. After compensating for data density changes by 'bootstrap' resampling, a median increase for all durations has also been confirmed for the (less rainy) Alpine area in northeastern Lombardy. Such changes in climatology have practical importance, because stationarity is a customary assumption when rainfall annual maxima frequencies are estimated for civil engineering. Abstract : It is shown, for the first time in Italy, that changes in rainfall annual maxima climatology are indeed present in the period 1950–2005. In the Alpine portion of Lombardy, the rainfall annual maxima distribution from 6‐ to 24‐h event durations significantly moves towards higher values when going from the period 1950–1977 to 1978–2005. An opposite change is found for 1‐h duration. Standard hypothesis tests are used; bootstrap resampling compensates for changes in data density.