•Instead of holistic trend model partial trend models are developed.•Practically applicable polygon trend analysis statistical values are derived.•The application of the new methodology is for three ...regions from World.•Transitional trend components are identified.
Trend analysis is continuously in the research and application agenda due to climate change effect searches on various engineering, social, economic, agriculture, environmental and water resources design, management, operation and management studies. Classical trend analysis is useable for holistic trend identification and then statistical quantification as for its intercept and slope. The main drawbacks in these classical approaches are the set of fundamental assumptions such as the serial independence of the given time series, pre-whitening, normality of the data and non-existence of serial comparison among different sections of the same record. This paper explains a non-parametric approach to avoid almost all these difficulties by simple methodology, which is referred as the Innovative Polygonal Trend Analysis (IPTA). Such an approach helps not only to identify the trend in a given series, but also trend transitions between successive sections of the two equal segments from the original hydro-meteorological time series leading to trend polygon, which provides a productive basis for finer interpretation with linguistic and numerical interpretations and inferences from a given time series. The application of the IPTA is presented for rainfall records from New Jersey, USA, Danube River and Göksu River discharge records from Romania and Turkey.
•Trend null hypothesis tests are not devised for exploratory analysis of hydrological data.•Adjusting procedures accounting for correlation in trend tests are insufficient or flawed.•Inductive trend ...tests cannot provide information on the nonstationarity of a process without a priori deductive arguments.
The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as ‘deterministic components’ or ‘trends’ even though the complexity of hydrological systems does not allow easy deterministic explanations and attributions. Consequently, trend estimation techniques have been developed to make and justify statements about tendencies in the historical data, which are often used to predict future events. Testing trend hypothesis on observed time series is widespread in the hydro-meteorological literature mainly due to the interest in detecting consequences of human activities on the hydrological cycle. This analysis usually relies on the application of some null hypothesis significance tests (NHSTs) for slowly-varying and/or abrupt changes, such as Mann-Kendall, Pettitt, or similar, to summary statistics of hydrological time series (e.g., annual averages, maxima, minima, etc.). However, the reliability of this application has seldom been explored in detail. This paper discusses misuse, misinterpretation, and logical flaws of NHST for trends in the analysis of hydrological data from three different points of view: historic-logical, semantic-epistemological, and practical. Based on a review of NHST rationale, and basic statistical definitions of stationarity, nonstationarity, and ergodicity, we show that even if the empirical estimation of trends in hydrological time series is always feasible from a numerical point of view, it is uninformative and does not allow the inference of nonstationarity without assuming a priori additional information on the underlying stochastic process, according to deductive reasoning. This prevents the use of trend NHST outcomes to support nonstationary frequency analysis and modeling. We also show that the correlation structures characterizing hydrological time series might easily be underestimated, further compromising the attempt to draw conclusions about trends spanning the period of records. Moreover, even though adjusting procedures accounting for correlation have been developed, some of them are insufficient or are applied only to some tests, while some others are theoretically flawed but still widely applied. In particular, using 250 unimpacted stream flow time series across the conterminous United States (CONUS), we show that the test results can dramatically change if the sequences of annual values are reproduced starting from daily stream flow records, whose larger sizes enable a more reliable assessment of the correlation structures.
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.
Recent trends, including survival beyond 30 days, in aortic valve replacement (AVR) following the expansion of indications for transcatheter aortic valve replacement (TAVR) are not well-understood.
...The authors sought to characterize the trends in characteristics and outcomes of patients undergoing AVR.
The authors analyzed Medicare beneficiaries who underwent TAVR and SAVR in 2012 to 2019. They evaluated case volume, demographics, comorbidities, 1-year mortality, and discharge disposition. Cox proportional hazard models were used to assess the annual change in outcomes.
Per 100,000 beneficiary-years, AVR increased from 107 to 156, TAVR increased from 19 to 101, whereas SAVR declined from 88 to 54. The median interquartile range age remained similar from 77 71-83 years to 78 72-84 years for overall AVR, decreased from 84 79-88 years to 81 75-86 years for TAVR, and decreased from 76 71-81 years to 72 68-77 years for SAVR. For all AVR patients, the prevalence of comorbidities remained relatively stable. The 1-year mortality for all AVR decreased from 11.9% to 9.4%. Annual change in the adjusted odds of 1-year mortality was 0.93 (95% CI: 0.92-0.94) for TAVR and 0.98 (95% CI: 0.97-0.99) for SAVR, and 0.94 (95% CI: 0.93-0.95) for all AVR. Patients discharged to home after AVR increased from 24.2% to 54.7%, primarily driven by increasing home discharge after TAVR.
The advent of TAVR has led to about a 60% increase in overall AVR in older adults. Improving outcomes in AVR as a whole following the advent of TAVR with increased access is a reassuring trend.
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