Trend detection by innovative polygon trend analysis for winds and waves

Abstract

It is known that densely populated coastal areas may be adversely affected as a result of the climate change effects. In this respect, for coastal protection, utilization, and management it is critical to understand the changes in wind speed (WS) and significant wave height (SWH) in coastal areas. Innovative approaches, which are one of the trend analysis methods used as an effective way to examine these changes, have started to be used very frequently in many fields in recent years, although not in coastal and marine engineering. The Innovative Polygon Trend Analysis (IPTA) method provides to observe the one-year behavior of the time series by representing the changes between consecutive months as well as determining the trends in each individual month. It is not also affected by constraints such as data length, distribution type or serial correlation. Therefore, the main objective of this study is to investigate whether using innovative trend methods compared to the traditional methods makes a difference in trends of the climatological variables. For this goal, trends of mean and maximum WS and SWH series for each month at 33 coastal locations in Black Sea coasts were evaluated. Wind and wave parameters WS and SWH were obtained from 42-year long-term wave simulations using Simulating Waves Nearshore (SWAN) model forced by the Climate Forecast System Reanalysis (CFSR). Monthly mean and maximum WS and SWH ​​were calculated at all locations and then trend analyses using both traditional and innovative methods were performed. Low occurrence of trends were detected for mean SWH, maximum SWH, mean WS, and maximum WS according to the Mann-Kendall test in the studied months. The IPTA method detected more trends, such as the decreasing trend of the mean SWH at most locations in May, July and November December. The lowest (highest) values were seen in summer (winter), according to a one-year cycle on the IPTA template for all variables. According to both methods, most of the months showed a decreasing trend for the mean WS at some locations in the inner continental shelf of the southwestern and southeastern Black Sea. The IPTA method can capture most of the trends detected by the Mann-Kendall method, and more missed by the latter method.

Wave Power Trends over the Mediterranean Sea Based on Innovative Methods and 60-Year ERA5 Reanalysis

“…In the improved visualization of ITA (IV-ITA) method proposed by Güçlü (2020) [51], the data can be statistically categorized as low values and high values by applying the Pettitt change point test to the differences of the time series that are divided into two halves and ranked in ascending order. Detailed information about the ITA, IPTA, IPTA with star graph, and IV-ITA methods can be found in Acar et al (2022Acar et al ( , 2023 [26,35] and Akçay et al (2022) [25].…”

“…In previous studies, researchers generally used the classical Mann-Kendall test, Theil-Sen method, and linear regression analysis. Apart from classical methods, the innovative trend analysis (ITA) method, suggested by Şen (2012) [34], the innovative polygon trend analysis (IPTA), the improved visualization of ITA (IV-ITA), and star graph methods based on ITA have been the basis for several recent studies (Acar et al, 2022(Acar et al, , 2023; Akçay et al, 2022) [25] and have provided greater clarity in the perception of long-term climatic trends. The innovative methods are open to the visual interpretation of the results, do not contain assumptions such as normal distribution and serial dependence, etc., and are easy to apply.…”

Trend Analysis and Projection of Climate Variables Using the LARS-WG Model in Coastal Areas of China

“…Moreover, this method is capable of uncovering monthly and seasonal variations that may not be detected by the traditional annual hydrometeorological analysis (Sen et al, 2019). Several studies currently apply the IPTA technique and suggest its capacity for exploring the hidden trends that occur during the holistic annual trend detection approach (Akcay et al, 2022;Esit, 2023;Sen et al, 2019). Akcay et al (2022) applied both IPTA and Man Kendall (MK) for trend analysis of winds and waves and indicated that the IPTA method is more sensitive and detects more trends than the MK method.…”

“…In the Shandong plain regions, the highest significant increasing trend is identified at the Dingtao station in January (39.3%), whereas the maximum significant decreasing trend is recorded at the Heze station in December (180.4%). Several studies currently apply the IPTA technique and suggest its capacity for exploring the hidden trends that occur during the holistic annual trend detection approach (Akcay et al, 2022;Esit, 2023;Sen et al, 2019). Akcay et al (2022) applied both IPTA and Man Kendall (MK) for trend analysis of winds and waves and indicated that the IPTA method is more sensitive and detects more trends than the MK method, which aligns with these study results.…”

“…Several studies currently apply the IPTA technique and suggest its capacity for exploring the hidden trends that occur during the holistic annual trend detection approach (Akcay et al, 2022;Esit, 2023;Sen et al, 2019). Akcay et al (2022) applied both IPTA and Man Kendall (MK) for trend analysis of winds and waves and indicated that the IPTA method is more sensitive and detects more trends than the MK method, which aligns with these study results. Comparing the ITA and MK methods, Alifujiang et al (2020) suggested that the ITA approach is capable of identifying hidden trends that are not detectable by the MK method.…”