Publication:
Trend detection by innovative polygon trend analysis for winds and waves

dc.contributor.buuauthorAkcay, Fatma
dc.contributor.buuauthorBingölbali, Bilal
dc.contributor.buuauthorBİNGÖLBALİ, BİLAL
dc.contributor.buuauthorAkpınar, Adem
dc.contributor.buuauthorAKPINAR, ADEM
dc.contributor.buuauthorKankal, Murat
dc.contributor.buuauthorKANKAL, MURAT
dc.contributor.departmentBursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.
dc.contributor.departmentBursa Uludağ Üniversitesi/İnegöl Meslek Yüksekokulu.
dc.contributor.orcid0000-0003-4496-5974
dc.contributor.orcid0000-0002-9042-6851
dc.contributor.orcid0000-0003-0897-4742
dc.contributor.researcheridAAZ-6851-2020
dc.contributor.researcheridAAC-6763-2019
dc.date.accessioned2024-10-02T06:11:49Z
dc.date.available2024-10-02T06:11:49Z
dc.date.issued2022-08-10
dc.description.abstractIt 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.
dc.identifier.doi10.3389/fmars.2022.930911
dc.identifier.urihttps://doi.org/10.3389/fmars.2022.930911
dc.identifier.urihttps://hdl.handle.net/11452/45636
dc.identifier.volume9
dc.identifier.wos000843931800001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherFrontiers Media Sa
dc.relation.journalFrontiers In Marine Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.relation.tubitak214M436
dc.subjectCoastal regions
dc.subjectOcean wind
dc.subjectModel
dc.subjectSwan
dc.subjectVariability
dc.subjectIdentification
dc.subjectSimulation
dc.subjectExtremes
dc.subjectHeight
dc.subjectMonthly trend analysis
dc.subjectInnovative polygon trend analysis
dc.subjectMann-kendall test
dc.subjectSignificant wave height
dc.subjectWind speed
dc.subjectBlack sea
dc.subjectScience & technology
dc.subjectLife sciences & biomedicine
dc.subjectEnvironmental sciences
dc.subjectMarine & freshwater biology
dc.titleTrend detection by innovative polygon trend analysis for winds and waves
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublicationeae8fad3-a39c-4f74-b0a3-dc174fdc76ad
relation.isAuthorOfPublication7613a1fe-c70a-4b3c-9424-e4d5cabe5d81
relation.isAuthorOfPublication875454d9-443c-4a31-9bce-5442b8431fdb
relation.isAuthorOfPublication.latestForDiscoveryeae8fad3-a39c-4f74-b0a3-dc174fdc76ad

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