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Summary: High percentages of the yearly total precipitation concentration in a few very rainy days can increase the risks of floods and soil instability. In this paper, an investigation of the spatial and temporal patterns of daily and monthly precipitation concentration in Calabria (Southern Italy) has been carried out by means of a homogenous daily precipitation data set. The results show that a very dishomogeneous temporal distribution of the daily rainfall characterizes the eastern side of the region with rain gauges in which one quarter of the rainiest days represents almost three quarters of the total pluviometric amount, and a western side which presents a more homogenous temporal distribution of the rainfall. Moreover, the application of the Mann–Kendall test has shown a clear tendency toward a weaker seasonality of the rainfall distribution. [ABSTRACT FROM AUTHOR]

Abstract: Rainfall variability is an important feature of semi-arid climates, and climate change is likely to increase that variability in many of these regions. An understanding of rainfall variability and trends in that variability is needed to help vulnerable dryland agriculturalists and policymakers address current climate variation and future climate change. The goals of this paper are to examine this climatic phenomenon in semi-arid Botswana, to investigate these results for signs of climate change, and to explore the policy implications for climate adaptation. To reach these goals, the paper determines rainfall variability and monthly and annual trends in that variability. The results agree with earlier work showing gradients in rainfall and rainfall variability across Botswana. The results also identify a trend towards decreased rainfall throughout the nation, which is associated with decreases in the number of rainy days. Both the drying trend and decrease in rainy days agree with climate change projections for southern Africa. The paper discusses policies that the government could adopt to help its farmers adapt to climate change. [ABSTRACT FROM AUTHOR]

Abstract: Winter rainfall on the Iberian Peninsula presents a significant decrease in its western and central areas throughout the second half of the 20th century, whereas over the eastern fringe it shows hardly any variation. This spatial distribution of precipitation trends are related to teleconnection indices trends and changes in the frequency of surface circulation patterns over Western Europe. We determined these circulation patterns by carrying out a daily objective synoptic classification by means of a principal components analysis (PCA) using sea-level pressure (SLP) data. In order to better understand Iberian winter precipitation trends, circulation patterns are associated with those teleconnection patterns that have an important influence on Iberian rainfall: Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and Western Mediterranean Oscillation (WeMO). The circulation pattern showing the biggest increases involves high pressure over Central Europe. This pattern is showing a positive AO and NAO phase, and simultaneously, a negative WeMO phase. [Copyright &y& Elsevier]

Accurate long-term estimates of rainfall at fine spatial and temporal resolution are vital for hydrometeorology and climatology studies, but such data are often unavailable in remote regions. We assessed the accuracy of three satellite-based precipitation products that have data from 1981 to 2019 over the state of Rondônia in the Brazilian Amazon: (a) satellite-only, using the Climate Hazards Group Infrared Precipitation (CHIRP) product, (b) CHIRP with sparse gauge data (CHIRPS), and (c) CHIRPS calibrated with data from a dense rain gauge network (N = 73) (dnCHIRPS). We evaluated the rainfall products using additional validation gauges (N = 55) at the monthly and seasonal time scales and compared their drought events and temporal trends. Both CHIRP (10.0 mm/month mean error (ME), 23.6% percent bias (PB)) and CHIRPS (−0.08ME, 7.4% PB) underestimate high monthly rainfall in the wet season and overestimate low monthly rainfall during the dry season. dnCHIRPS had a lower error in monthly rainfall (−0.01ME, 1.1%PB) compared with CHIRP and CHIRPS, with the largest percentage difference between dnCHIRPS and the other two datasets in the dry season. dnCHIRPS captured decreasing trends in dry season rainfall over agricultural parts of the state, trends that were missed by the other two products. We conclude that a high density of rain gauges is essential for documenting the spatial pattern and trends in rainfall during the dry season and droughts in this important agricultural region of the Amazon basin. [ABSTRACT FROM AUTHOR]

Since several datasets are available with marked differences, the assessment of precipitation data is a key aspect to support the choice of the most adequate precipitation product for a certain research or operational application. In the present study, we evaluated the use of the daily rainfall dataset from CHIRPS with spatial resolution of 0.05° for different purposes in the states of the Brazilian Legal Amazon. We compared monthly rainfall, annual rainfall indices and their trends calculated using CHIRPS data and rain gauge observations with a point-to-pixel analysis. The use of daily CHIRPS data provided mean monthly rainfall similar to that obtained using data from the rain gauge stations, but CHIRPS data tend to underestimate the values for the rainiest months. The correlation was usually lower in the western Amazon, especially during its rainy season. The same underestimation was observed for extreme rainfall indices. CHIRPS product produces more similar results to rain gauge data for the indices PRCPTOT, nP, and R95pad, while strong underestimate the most extreme rainfall indices (R50mm, Rx1day, Rx5days). For the 45 stations and 15 rainfall indices analysed, 63 significant trends were detected using rain gauge data, of which only 13 were detected using CHIRPS product. Therefore, the use of CHIRPS data does not well represent the trends in rainfall indices. • Use of the rainfall dataset from CHIRPS for different purposes in Brazilian Amazon. • CHIRPS data tend to underestimate the rainfall for the rainiest months. • The correlation was greater for the rainy season in eastern Amazonia. • CHIRPS data underestimate the extreme rainfall indices. [ABSTRACT FROM AUTHOR]

Trend detection in hydro-climatological time series is a prime task under the context of climate changes. Rainfall is a key component of the water cycle and its variability can profoundly influence agriculture, ecosystems and water resource management. In this paper, an innovative trend analysis (ITA) method with a significant test is used for rainfall trend detection at 14 stations in the Yangtze River Delta (YRD) during 1961–2016. The trends are separately evaluated for low (<10th percentile), medium (10th–90th percentile) and high (>90th percentile) rainfall at the annual and seasonal scales. The slope and significance of the rainfall trends derived from the ITA method are compared with those from the classical trend analysis methods Theil-Sen approach and Mann-Kendall test, respectively. The ITA shows significant increasing trends at the 99% confidence level in annual rainfall at all stations. While the same significant increasing trends are identified for summer and winter, decreasing trends dominate in spring and autumn. Contrasting trends are found for extreme rainfall with strong increasing trends in high rainfall in summer and winter and decreasing trends in low rainfall in spring and autumn. The results of the ITA confirmed by the classical trend analysis methods call for more attention to the risk management of flood in extreme seasons and drought in transitional seasons across the YRD region. • Trends analysis of rainfall across the Yangtze River Delta is performed by the innovative trend analysis (ITA) method. • Separate trends in low, medium and high rainfall are quantitatively calculated based on the ITA method. • Some advantages of the ITA method to classical trend methods (Mann-Kendall test and Theil-Sen approach) are presented. • High rainfall in summer showed significant increase while low rainfall in spring and autumn exhibited decrease. [ABSTRACT FROM AUTHOR]