Application of the Innovative Trend Analysis Method for the Trend Analysis of Rainfall Anomalies in Southern Italy

Caloiero, Tommaso; Coscarelli, Roberto; Ferrari, Ennio

doi:10.1007/s11269-018-2117-z

Cited by 126 publications

(41 citation statements)

References 26 publications

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“…The visual display provides primary insights into observing changes, that is, increasing, decreasing or no trend for different clusters. In Figure , a confidence bounds of 0.10 has been added as the distance from the line 1:1 to visualize the distance of the points from the no‐trend line but without any statistical meaning (Caloiero et al ., ). Rainfall points located between the ±10% lines indicates insignificant variations and may be attributed to natural variability or randomness.…”

Section: Resultsmentioning

confidence: 97%

Recent rainfall trend over Sri Lanka (1987–2017)

Nisansala

Abeysingha

Islam

et al. 2019

Intl Journal of Climatology

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Rainfall trend analysis provides useful information for effective planning, and management of water resources, and gives an insight into climate change of a region. This study investigates trends in annual and seasonal rainfall over Sri Lanka using an Innovative Trend Analysis (ITA) and Mann–Kendall (MK) test with Sen's slope estimator. The MK test showed increasing trends in annual rainfall at 24 stations (65%) with five stations showing significant increasing trend. Annual rainfall at 13 locations (35%) showed decreasing trend, but none were significant (p < .05). ITA results for annual rainfall showed increasing trend at 67% stations while 33% stations showed decreasing trend. MK test results for seasonal rainfall indicated increasing trend at 76, 51, 32, and 86% of stations during First Inter‐Monsoon (FIM), Second Inter‐Monsoon (SIM), South West Monsoon (SWM), and North East Monsoon (NEM) seasons, respectively. Seasonal analysis of rainfall trend using ITA method showed increasing trend in 81, 70, 32, and 65% stations during FIM, SIM, SWM, and NEM, respectively. ITA and MK tests exhibited similar trend results for 80% of the stations. Moreover, Spearman's rho correlation coefficient between ITA and MK test trends showed significant (p < .05) positive correlation. In general, eastern, south eastern, north and north central regions of the country showed increasing rainfall trend over the last 31 years (1987–2017) while western, part of north western and central part of the country indicated a decreasing rainfall trend during the same period.

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Section: Resultsmentioning

confidence: 97%

Recent rainfall trend over Sri Lanka (1987–2017)

Nisansala

Abeysingha

Islam

et al. 2019

Intl Journal of Climatology

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“…Then, there were significant decreasing (increasing) trends in seasonal (spring, summer, autumn, winter). In the previous years, throughout the world, various examinations have been led to trend analysis of meteorological data utilizing parametric and non-parametric methods, for example [63][64][65][66][67][68]. The spatial variability of trend is very important for impact assessments and adaptation of planning for floods, droughts, and extreme events [1,68] in the study region.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Trends and Seasonality Detection of the Observed Flow in Yangtze River Using Mann-Kendall and Sen’s Innovative Trend Method

Ali

Kuriqi

Abubaker³

et al. 2019

Water

191

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Trend analysis of streamflow provides practical information for better management of water resources on the eve of climate change. Thus, the objective of this study is to evaluate the presence of possible trends in the annual, seasonal, maximum, and minimum flow of Yangtze River at Cuntan and Zhutuo stations in China for the period 1980 to 2015. The assessment was carried out using the Mann–Kendall trend test, and the innovative trend analysis, while Sen’s slope is used to estimate the magnitude of the changes. The results of the study revealed that there were increasing and decreasing trends at Cuntan and Zhutuo stations in different months. The mean annual flow was found to decrease at a rate of −26.76 m3/s and −17.37 m3/s at both stations. The minimum flow was found to significantly increase at a rate of 30.57 m3/s and 16.37 m3/s, at a 95% level of confidence. Maximum annual flows showed an increasing trend in both regions of the Yangtze River. On the seasonal scale, the results showed that stations are more sensitive to seasonal flow variability suggesting a probable flooding aggravation. The winter season showed an increasing flow trend, while summer showed a decreasing trend. The spring flow was found to have an increasing trend by the Mann–Kendall test at both stations, but in the Zhutuo Station, a decreasing trend was found by way of the innovative trend analysis method. However, the autumn flow indicated a decreasing trend over the region by the Mann–Kendall (MK) test at both stations while it had an increasing trend in Cuntan by the innovative trend analysis method. The result showed nonstationary increasing and decreasing flow trends over the region. Innovative trend analysis method has the advantage of detecting the sub-trends in the flow time series because of its ability to present the results in graphical format. The results of the study indicate that decreasing trends may create water scarcity if proper adaptation measures are not taken.

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“…By looking at the seasonal correlations, we see that the performance is almost the same in the spring season, while it worsened for the other seasons; most significantly, it worsened for winter months, where it went from a good correlation to anti-correlation. In the 1981-2010 time period, there was an increase in extreme precipitation and a decrease in the number of precipitation days in Calabria [80], especially in winter [81]. This change in precipitation patterns might have escaped the HRM climatology that drove these RCMs; even though the 30-years climatology of precipitation (i.e., the decrease of the yearly average) might have been correctly reproduced (see Appendix A Table A2).…”

Section: Discussionmentioning

confidence: 99%

Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)

2019

Self Cite

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Skills in reproducing monthly rainfall over Calabria (southern Italy) have been validated for the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) satellite data, the E-OBS dataset and 13 Global Climate Model-Regional Climate Model (GCM-RCM) combinations, belonging to the ENSEMBLES project output set. To this aim, 73 rainfall series for the period 1951–1980 and 79 series for the period 1981–2010 have been selected from the database managed by Multi-Risk Functional Centre of the Regional Agency for Environmental Protection (Regione Calabria). The relative mean and standard deviation errors, and the Pearson correlation coefficient have been used as validation metrics. Results showed that CHIRPS satellite data (available only for the 1981–2010 validation period) and RCMs based on the ECHAM5 Global Climate performed better both in mean error and standard deviation error compared to other datasets. Moreover, a slight appreciable improvement in performance for all ECHAM5-based models and for the E-OBS dataset has been observed in the 1981–2010 time-period. The whole validation-and-assessment procedure applied in this work is general and easily applicable where ground data and gridded data are available. This procedure might help scientists and policy makers to select among available datasets those best suited for further applications, even in regions with complex orography and an inadequate amount of representative stations.

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Application of the Innovative Trend Analysis Method for the Trend Analysis of Rainfall Anomalies in Southern Italy

Cited by 126 publications

References 26 publications

Recent rainfall trend over Sri Lanka (1987–2017)

Recent rainfall trend over Sri Lanka (1987–2017)

Long-Term Trends and Seasonality Detection of the Observed Flow in Yangtze River Using Mann-Kendall and Sen’s Innovative Trend Method

Validation of Satellite, Reanalysis and RCM Data of Monthly Rainfall in Calabria (Southern Italy)

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