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The present study is designed to analyse the annual rainfall variability and trend in 30 meteorological stations of the Asir region for the period of 1970–2017 using the Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) test, trend free pre-whitening Mann-Kendall (TFPW MK) test, and the innovative trend analysis (ITA). A comparative study among the trend detection techniques was performed using the correlation coefficient. The future rainfall trend based on the historical rainfall pattern was investigated by using detrended fluctuation analysis (DFA). Results of the MK test showed that 20 stations in the study area observed a negative trend, and out of these, nine stations had significant negative trends at the significance level of 0.01. The findings of the MMK test showed that 23 stations recorded negative trends, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the TFPW-MK test, 21 stations observed a negative trend, and among these, 10 stations had significant negative trends at the significance of 0.01. ITA detected 25 stations observing a negative trend, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the tests and their performance, the MMK test appeared as the best performing technique among the MK test family, while ITA appeared as the best trend detection technique among the four techniques because it outperformed (p < 0.01) the others. Results of DFA showed that 23 stations (10 were significant) had recorded declining future rainfall trends based on past trends. The results of the present study would help the planners and policy makers to make accurate and easy decisions on irrigation, climatic, and water resource management in the Asir region of Saudi Arabia.

Research and applications on trend analysis have recently been on the agenda and are top priorities in many disciplines due to the effects of climate change. After a thorough evaluation of the literature, it is noted that different hydro-meteorological variables, such as precipitation, temperature, etc., are studied and analyzed individually. This research proposes a new innovative polygon trend analysis application (S-IPTA) using the standardization concept to fill this gap in classical trend applications and comprehensively compare the trends of different variables to temporal and spatial patterns. Firstly, using statistical standardization, S-IPTA adjusts the original data sets and makes them dimensionless. Then, the innovative trend analyses are conducted and interpreted on one single graph (S-IPTA). The S-IPTA methodology is applied to monthly precipitation and temperature time series of Konya Basin in Türkiye at ten meteorological stations between 1959 and 2022. For precipitation, the S-IPTA did not exhibit a consistent polygon across all stations within the study area, while the temperature polygon was more regular, indicating that the temperature mean was generally stable with a positive trend. Also, S-IPTA shows the difference between the average value for each month and the newly proposed long-term average value (0). S-IPTA also provides a basis for a better interpretation of climate change and its effects by providing a common denominator for various trend characteristics, such as trend magnitudes and trend transitions in different hydro-meteorological time series.

In this paper, an investigation of the temporal rainfall variability, in a large area of southern Italy, has been carried out using a homogeneous monthly rainfall dataset of 559 rain gauges with more than 50 years of observation. The area under investigation is a large portion of the Italian peninsula, ranging from the Campania and the Apulia regions in the North, to Sicily in the South, and covering an area of about 85,000 km2. Possible trends in seasonal and annual rainfall values have been detected by means of a new graphical technique, Şen’s method, which allows the trend identification of the low, medium and high values of a series. Moreover, the Mann–Kendall test has been also applied. As a result, different values and tendencies of the highest and of the lowest rainfall data have emerged among the five regions considered in the analysis. In particular, at seasonal scale, a negative trend has been detected especially in winter and in autumn in the whole study area, whereas not well defined trend signals have been identified in summer and spring.

Drought indices, such as the Standardized Precipitation Index (SPI) and the Stream Flow Drought Index (SDI), are mathematical indicators that represent an overall decrease in average amounts of rainfall over a specific period of time. The changing values of SPI and SDI can be determined by trend analysis and can help decision makers in estimating and managing the future values of water resources on issues such as dam management and energy production. In this study, in addition to SPI12 and SDI12, trend analyzes of monthly precipitation and stream flow data affecting drought were also conducted. Mann–Kendall Test (MK), Spearman's Rho test, Sen-Innovative Trend Analysis (ITA) were chosen as trend analysis. As a result of the analysis, in precipitation performed with MK, an increasing trend at 95% significance level was detected in January, while no trend was found in the other months. While increasing trends were found in all months using SPI12, no trend was detected in SDI12. In Spearman's Rho test, no trend was detected in SDI12 and precipitation for all months, whereas increasing trend for January, February and April were detected for SPI12 and January for streamflow data. The analysis made with ITA was evaluated in two parts, graphically and statistically. The graphical method was carried out for monthly data. In statistical evaluation of ITA for SPI12 and SDI12, increasing trends were detected for all monthly data, however, in the graphical analysis, different results were obtained for each month, which did not fully support the results of the statistical analysis. Graphical abstract

In this study, the trend of monthly mean and annual streamflow values of 16 streamflow gauge stations in the Tigris basin, which holds 13% water potential for Turkey, is determined. The monotonic trends are calculated using non-parametric Mann−Kendall (MK) test. To remove serial correlation from time series, a modified ‘pre-whitening’ method is used. The trend slopes are determined by Sen’s slope method. Moreover, innovative trend analysis (ITA) method is also used to determine trends of low, medium and high streamflow values. As a result of the study, trend indicators, namely, Z values of MK tests and D values of the ITA method are compared and it has been observed that the trend directions by MK and ITA are generally similar. Nevertheless, only the negative D values calculated by ITA are mostly higher than the negative Z values calculated by MK. The ITA results of the annual mean streamflow show 80% of the stations show a strong decrease in trend for high values. Most of the significant trends found by MK are obtained in a decreasing direction, and the linear slopes are mostly determined in the range of ±2% per year. According to the spatial analysis, the significant decreasing trends are generally located in the middle region of the basin.

Temporal trends in comparative meta-analyses of interventions are well-recognized in the medical literature. For studies of diagnostic test accuracy (DTA), evidence of temporal trends is growing and the importance of assessing and reporting them has been highlighted in recent guidelines on postmarket surveillance in several jurisdictions. In this study, we evaluate the prevalence and patterns of time trends using a larger and more up-to-date set of DTA systematic reviews than has previously been examined, from the Cochrane Database of Systematic Reviews. Cumulative meta-analysis was conducted on bivariate random effects meta-analysis estimates of sensitivity and specificity, after ranking studies by publication date. Trends for all studies were assessed graphically using plots of summary estimates by study rank, and using receiver operating characteristic plots of sensitivity vs specificity. Linear trends were also described using weighted linear regression with autocorrelated errors of summary estimates against study rank. Various patterns of nonlinear trends were characterized descriptively. The analysis included 46 reviews (92 meta-analyses) conducted between 2017 and 2022. The total number of studies within all reviews was 1486, with a median (IQR) 7134 (2782–16,406) participants per review. Reviews had a median (IQR) time span of 19 (15-25) publication years. Time trends in at least 1 DTA measure were observed in 40 (87%) reviews, and statistically significant linear trends in 32 (70%) reviews. Nonlinear time trends were observed in 14 (30%) reviews. There was no evidence for a trend in either DTA measure in 6 (13%) reviews. The study contributes evidence on the variety in patterns of linear and nonlinear temporal DTA trends which has not previously been described. We recommended researchers check statistical assumptions of trend analysis methods, eg, using graphical methods. Further research into potential reasons for time trends could contribute to the robustness of future meta-analyses.

Coastal areas are increasingly endangered by climate change and associated sea level rise, which could have serious consequences, such as shoreline erosion and coastal city submergence. The current study aims to conduct a historical trend analysis (HTA) and predict the shoreline changes of the Nile Delta coasts. The Digital Shoreline Analysis System (DSAS) software, with the GIS environment, is used for monitoring the shoreline changes using a number of statistical methods (SCE, NSM, EPR, WLR and LRR). Satellite images from 1974 to 2022 were collected and geometrically corrected using supervised classification to detect the shoreline change of the Nile Delta. The GIS was used for detecting and monitoring changes in the shoreline, as well as forecasting future changes in the shoreline for the next 10 and 20 years (2033–2043). The critical sections of the Nile Delta were identified, and a time series analysis of shoreline changes was conducted. For each section, linear equations were established to predict probable changes in the shoreline. Between 1974 and 2022, the shoreline of the Nile Delta moved inland in different directions due to coastal erosion, and predictions indicate that this erosion will continue until both 2033 and 2043, particularly affecting the Rosetta and Damietta sections. The erosion rate ranged between 30–60 and 10–25 m/year at Rosetta and Damietta, respectively, but at Manzala, it ranged between 8–15 m/year. Continued erosion of the Nile Delta shoreline could have severe consequences that could affect the inhabitants, economy, buildings, roads, railways, and ports. These areas need an integrated coastal management strategy which incorporates increasing consciousness, urban development, and the implementation of rules and adaptation plans. The results of the current study and forecasting the shoreline change could help in protecting such areas.

This study investigated the annual and seasonal rainfall variability at five selected stations of Amhara Regional State, by using the innovative trend analysis method (ITAM), Mann-Kendall (MK) and Sen’s slope estimator test. The result showed that the trend of annual rainfall was increasing in Gondar (Z = 1.69), Motta (Z = 0.93), and Bahir Dar (Z = 0.07) stations. However, the trends in Dangla (Z = −0.37) and Adet (Z = −0.32) stations showed a decreasing trend. As far as monthly and seasonal variability of rainfall are concerned, all the stations exhibited sensitivity of change. The trend of rainfall in May, June, July, August, and September was increasing. However, the trend on the rest of other months showed a decreasing trend. The increase in rainfall during Kiremt season, along with the decrease in number of rainy days, leads to an increase of extreme rainfall events over the region during 1980–2016. The consistency in rainfall trends over the study region confirms the robustness of the change in trends. Innovative trend analysis method is very crucial method for detecting the trends in rainfall time series data due to its potential to present the results in graphical format as well. The findings of this paper could help researchers to understand the annual and seasonal variability of rainfall over the study region and become a foundation for further studies.

Water is a scarce resource on the earth, and the study of variability in precipitation is necessary for solving the issues related to water. Proper understanding of trends in annual precipitation is crucial for better management of available water sources. In the current study, the Mann–Kendall (MK) and innovative trend analysis (ITA) methods were used to detect the trends in annual rainfall for 30 synoptic stations and different meteorological subdivisions for the period of 1951–2020 for Karnataka, India. The significance of trend is also identified using the MK trend test. Furthermore, the entire time series data were classified into three sub groups to recognize the monotonic trend components based on the ITA method. The results of the MK trend test indicated that, in the first half of the time series data more stations showed decreasing trend, while in the second half, an increasing trend in annual precipitation was observed for most of the stations. In the ITA method, except for four stations, all the stations detected a significantly positive trend in annual precipitation. The spatially distributed trend for precipitation pattern for the total time series data showed the highest positive and negative trends in the southern and northern parts of the state, respectively. Six stations, namely, Bidar, Dakshina Kannada, Dharwad, Kalburgi, Udupi, and Vijayapura, exhibited instability in calculated trends. In the analysis of monotonic trends, only six stations showed monotonic trend components, while the other 24 stations and subdivisions are non-monotonic. In the current study, it was concluded that the ITA method is more sensitive than the MK trend test for detecting hidden trends.