{"title":"Innovative drought analysis via groundwater information","authors":"Veysi Kartal","doi":"10.1016/j.pce.2025.103901","DOIUrl":null,"url":null,"abstract":"<div><div>Drought hazard has complicated features related to climatic and spatio-temporal characteristics, making it challenging to accurately identify and track. Contemporary approaches to drought monitoring generally use standardized drought indices due to their practical utility. Despite the availability of a various array of drought indices, their application introduces complexities in data mining and decision-making processes, potentially resulting in confused outcomes. However, this research developed a new hybrid drought index Multivariate Cluster Ensemble Drought Evaluation Index (MCEDEI) based on machine learning technique cluster analysis using groundwater data of the KB region of Türkiye to assess the groundwater drought. For the development of MCEDEI, this study used 540-time series observations (range: 1978–2022) of groundwater data from five stations to evaluate drought characteristics. Furthermore, this study used steady-state probability to determine the trend and long-term probabilities of the drought index in the KB region of Türkiye. The results show that the NN (near normal) class was found to be dominant with a probability of 70.41% on a 1-month time scale, while NN was found to be dominant with a high probability of 65.94% on a 3-month time scale. The probability of the NN class was found to be equally high when the time scale was extended to 6, 9 and even 48 months. MD (moderate drought) remains important, and SD (severe drought) increases compared to SW (severe wet) classes. Findings shpw that there are significant changes in groundwater behaviour at different time scales. Short-term stability is characterized by the dominance of the NN class, while long-term scales show a trend towards extreme dry and wet conditions with a decrease in neutrality. As a result, Türkiye may face drought challenges in the future based on the findings.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"139 ","pages":"Article 103901"},"PeriodicalIF":3.0000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics and Chemistry of the Earth","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474706525000518","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Drought hazard has complicated features related to climatic and spatio-temporal characteristics, making it challenging to accurately identify and track. Contemporary approaches to drought monitoring generally use standardized drought indices due to their practical utility. Despite the availability of a various array of drought indices, their application introduces complexities in data mining and decision-making processes, potentially resulting in confused outcomes. However, this research developed a new hybrid drought index Multivariate Cluster Ensemble Drought Evaluation Index (MCEDEI) based on machine learning technique cluster analysis using groundwater data of the KB region of Türkiye to assess the groundwater drought. For the development of MCEDEI, this study used 540-time series observations (range: 1978–2022) of groundwater data from five stations to evaluate drought characteristics. Furthermore, this study used steady-state probability to determine the trend and long-term probabilities of the drought index in the KB region of Türkiye. The results show that the NN (near normal) class was found to be dominant with a probability of 70.41% on a 1-month time scale, while NN was found to be dominant with a high probability of 65.94% on a 3-month time scale. The probability of the NN class was found to be equally high when the time scale was extended to 6, 9 and even 48 months. MD (moderate drought) remains important, and SD (severe drought) increases compared to SW (severe wet) classes. Findings shpw that there are significant changes in groundwater behaviour at different time scales. Short-term stability is characterized by the dominance of the NN class, while long-term scales show a trend towards extreme dry and wet conditions with a decrease in neutrality. As a result, Türkiye may face drought challenges in the future based on the findings.
期刊介绍:
Physics and Chemistry of the Earth is an international interdisciplinary journal for the rapid publication of collections of refereed communications in separate thematic issues, either stemming from scientific meetings, or, especially compiled for the occasion. There is no restriction on the length of articles published in the journal. Physics and Chemistry of the Earth incorporates the separate Parts A, B and C which existed until the end of 2001.
Please note: the Editors are unable to consider submissions that are not invited or linked to a thematic issue. Please do not submit unsolicited papers.
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(geology, geochemistry, tectonophysics, seismology, volcanology, palaeomagnetism and rock magnetism, electromagnetism and potential fields, marine and environmental geosciences as well as geodesy).
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(hydrology and water resources research, engineering and management, oceanography and oceanic chemistry, shelf, sea, lake and river sciences, meteorology and atmospheric sciences incl. chemistry as well as climatology and glaciology).
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(solar, heliospheric and solar-planetary sciences, geology, geophysics and atmospheric sciences of planets, satellites and small bodies as well as cosmochemistry and exobiology).