Effect of Time-Resolution of Rainfall Data on Trend Estimation for Annual Maximum Depths with a Duration of 24 Hours

The main challenge of this paper is to demonstrate that one of the most frequently conducted analyses in the climate change field could be affected by significant errors, due to the use of rainfall data characterized by coarse time-resolution. In fact, in the scientific literature, there are many studies to verify the possible impacts of climate change on extreme rainfall, and particularly on annual maximum rainfall depths, H-d, characterized by duration d equal to 24 h, due to the significant length of the corresponding series. Typically, these studies do not specify the temporal aggregation, t(a), of the rainfall data on which maxima rely, although it is well known that the use of rainfall data with coarse t(a) can lead to significant underestimates of H-d. The effect of t(a) on the estimation of trends in annual maximum depths with d = 24 h, H-d=(24 h,) over the last 100 years is examined. We have used a published series of H-d=(24 h) derived by long-term historical rainfall observations with various temporal aggregations, due to the progress of recording systems through time, at 39 representative meteorological stations located in an inland region of Central Italy. Then, by using a recently developed mathematical relation between average underestimation error and the ratio t(a)/d, each H-d=(24 h) value has been corrected. Successively, commonly used climatic trend tests based on different approaches, including least-squares linear trend analysis, Mann-Kendall, and Sen's method, have been applied to the uncorrected and corrected series. The results show that the underestimation of H-d=(24 h) values with coarse t(a) plays a significant role in the analysis of the effects of climatic change on extreme rainfalls. Specifically, the correction of the H-d=(24 h) values can change the sign of the trend from positive to negative. Furthermore, it has been observed that the innovative Sen's method (based on a graphical approach) is less sensitive to corrections of the H-d values than the least-squares linear trend and the Mann-Kendall method. In any case, the analysis of H-d series containing potentially underestimated values, especially when d = 24 h, can lead to misleading results. Therefore, before conducting any trend analysis, H-d values determined from rainfall data characterized by coarse temporal resolution should always be corrected.

Automated summary

The main finding of this paper is that using rainfall data with coarse temporal resolution can lead to underestimation errors in extreme rainfall estimation and affect the analysis of climate change impacts. It is worth noting that different climate trend analysis methods may produce different results.