A Homogeneous Dataset for Rainfall Trend Analysis in the Calabria Region (Southern Italy)

Abstract

In order to investigate the tendency in rainfall amount in Calabria (southern Italy), in this work, monthly rainfall series were first tested for homogeneity and then a trend analysis was performed. In particular, a homogenization approach based on the Climatol method was applied to homogenize monthly climatological series. Then, the Mann–Kendall non-parametric test and the Theil–Sen estimator were applied to evaluate the presence of trends and their significance in the monthly, seasonal and annual rainfall series. Moreover, the trend slopes were further evaluated with a linear regression analysis. At the annual scale, results evidenced a decreasing trend mainly in the north-eastern part of the region. At the seasonal scale, a spatial distributed negative trend in winter, and a positive trend in summer, mainly localized in the north-western part of the region, were identified. Finally, on a monthly scale negative trends spreading across the region were detected in January and December, with an opposite behavior in July and especially in September, when almost the entire region presented a positive trend.

A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

“…That study was constructed using homogeneous sampling followed by a rigorous reliability process. There are several works where homogeneity is considered for rainfalls [30][31][32][33].…”

Innovative Trend Analysis of Air Temperature and Precipitation in the Jinsha River Basin, China

“…Without a homogeneity test, the impact of nonclimatic factors can mask or even mismatch the real climate change in the time series. Thus, a homogeneity test is important and essential for trend analysis studies of hydrometeorological variables [54]. The run homogeneity test is expressed as follows [31]: In this study, the run homogeneity test proposed by Swed and Wisenhart [53] was applied to determine the homogeneity of the data at a 5% significance level [31].…”

“…The run homogeneity test is expressed as follows [31]: In this study, the run homogeneity test proposed by Swed and Wisenhart [53] was applied to determine the homogeneity of the data at a 5% significance level [31]. When the variations in climatic factor series only result from the changes in climate and air, the climate record is considered homogeneous [54]. However, nonclimatic factors such as station relocations, changes in site conditions (land use/forest growth), modifications in the instrumentation and recalibrations, or variations in observation procedures, often affect the observed time series [55].…”

“…Without a homogeneity test, the impact of nonclimatic factors can mask or even mismatch the real climate change in the time series. Thus, a homogeneity test is important and essential for trend analysis studies of hydrometeorological variables [54]. The run homogeneity test is expressed as follows [31]:…”

Validation metrics of homogenization techniques on artificially inhomogenized monthly temperature networks in Sweden and Slovenia (1950–2005)

“…This approach is based on the assumption that the same climatic signal influences neighboring stations, and thus inhomogeneities can be identified considering the differences between these stations 17 . In relative homogeneity testing, the time series of the station being tested (candidate station) is compared to the ones of multiple surrounding stations (reference stations) either in a pairwise fashion or to a single composite reference time series computed from multiple neighboring stations 18 . Two well-known examples of homogeneous relative tests are the Standard Normal Homogeneity Test (SNHT 19 ) and the Craddock test 20 .…”