Innovative trend analysis of rainfall in relation to soybean productivity over western Maharashtra

This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman’s rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman’s rho, simple linear regression and Sen’s slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11 -time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra.

On the Use of an Innovative Trend Analysis Methodology for Temporal Trend Identification in Extreme Rainfall Indices Over the Central Highlands, Vietnam

Understanding past changes in the characteristics of climate extremes (such as frequency, intensity, and duration) forms an essential part of viable countermeasures to cope with climate-induced risks under a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands of Vietnam by employing the Şen’s innovative trend analysis (ITA) method in conjunction with the well-defined extreme rainfall indices developed by the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). The outcomes show that the overall trends in most extreme rainfall indices exhibited significant increases at several stations. Moreover, the high-value subgroups of most analyzed indices (such as maximum 5-day precipitation amount (Rx5day), simple daily intensity index (SDII), very wet days (R95p), extremely wet days (R99p), number of extremely heavy precipitation days (R50mm), and consecutive dry days (CDD)) were characterized mainly by significant increasing trends, thereby implying that heavy rainfall events have become more frequent and intense over recent decades. Some stations also exposed significant increasing trend behaviors in a given extreme index within all low-, medium-, and high-value subgroups. In general, it is expected that these findings yield more insightful knowledge on rainfall extremes to local decision-makers and other stakeholders.

Global Trends in the Occurrence and Characteristics of Blocking Anticylones Using Sen Innovative Trend Analysis

Atmospheric blocking plays an important role in modulating mid-latitude weather, in particular in the Northern Hemisphere (NH). Trend analysis of atmospheric blocking for both hemispheres by using Şen’s Innovative Trend Analysis (ITA) is performed in this study. The blocking data archived in the University of Missouri covers the period of 1968–2019 for the NH and 1970–2019 for the Southern Hemisphere is used in the study. Block occurrence, duration and blocking intensity (BI) is analysed by classifying the NH (and SH) into three groups according to the preferred blocking locations: Atlantic, Pacific and Continental (Atlantic, Pacific and Indian). In the NH, blocking intensity showed mixed results. It showed a decreasing trend for the entire hemisphere and Atlantic Region, whilst a different trend was shown for different BI clusters. For blocking numbers and duration, the entire hemisphere and regions showed increasing trends. These increasing trend values were also statistically significant. In the SH, blocking intensity showed a decreasing trend for low clusters, whilst medium and high cluster increased for the entire hemisphere. Block duration showed an increasing trend for the entire SH. Block numbers showed increasing trends, except for one point in the low cluster. Blocking characteristics showed different trends for different preferred blocking locations. Increasing trends of blocking numbers for the overall SH and Pacific region are statistically significant at 95% level. Increasing trends of blocking duration for the overall SH, Atlantic and Pacific region are statistically significant at 90%, 95% and 95% level, respectively.

Comparison of Sub-Series with Different Lengths Using Şen-Innovative Trend Analysis

Climate change causes trends in hydro-meteorological series. Traditional trend analysis methods such as Mann-Kendall and Spearman Rho are sensitive to correlated series and cannot detect non-parametric trends. Şen-innovative trend analysis method is launched to literature in order to overcome these restrictions. It does not require any restrictive assumptions as serial dependence and normal distribution and examines a main series as equally divided two sub-series. Şen multiple innovative trend analyses methodology is improved to detect partial trends on different sub-series but again equal lengths. Climate change nowadays more effects hydro-meteorological parameters according to last two or three decades and gives asymmetric trend change point on main time series. Due to asymmetric trend change points, it may be necessary to analyze sub-series with different lengths to use all measured data. In this study, Şen innovative trend analyses method is revised for these requirements (ITA_DL). The new approach compared with traditional Mann Kendall (MK) and Şen innovative trend analysis (Şen_ITA) gives successful and consistent results. ITA_DL gives four monotonic trends on Oxford May, July, September and October rainfall series although MK gives three monotonic trends on May, July and December and cannot detect trends on September and October. In the ITA_DL visual inspection, the December rainfall series does not show a trend that is monotonic or non-monotonic. Şen_ITA trend results are consistent with ITA_DL except September, although there are different trend slopes.