Identification of sinkhole-prone zones by successive coincidence deficit index analysis

ŞAHİN A. U., ÖZKAYA A.

HYDROLOGICAL PROCESSES, sa.6, 2024 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1002/hyp.15208
  • Dergi Adı: HYDROLOGICAL PROCESSES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Hacettepe Üniversitesi Adresli: Evet

Özet

Hydrological data-driven models require the time series of several hydrological events with different time resolutions. The interpretation of any time series event is generally difficult without some sort of filtering or converting it to a single index value. The simultaneous analysis of two or more hydrological events over a definite time span may be more informative about the region of interest. For this purpose, a new index, referred to as the successive coincidence deficit index (SCDI), was introduced to identify sinkhole-prone regions using the persistent water deficit concept. In this study, monthly integrated multi-satellite retrievals for GPM based precipitation (P) and gravity recovery and climate experiment-based groundwater storage (GWS) datasets over Konya Closed Basin (KCB) in T & uuml;rkiye were used to analyse the sinkhole occurrence. The main finding of this study is that SCDI distribution with high index values, concentrated on the southwestern part of KCB, is in line with the sinkholes occurred mainly after 2010. The proposed SCDI could also serve as a kind of drought index, which enables practitioners to quantify the relationship between drought and sinkhole occurrence. Moreover, the event coincidence analysis was utilized to detect deficiency in GWS over the KCB, which was associated with a rate of 0.8 for P deficiency, and this rate reaches up to 0.9 in the sinkhole region analysed in this study. As a conclusion, the proposed methodology can detect sinkhole-prone regions to construct risk maps for stakeholders, policymakers, and end users. A new index, referred to as the successive coincidence deficit index (SCDI), was introduced to identify sinkhole-prone regions using the persistent water deficit concept. The proposed SCDI could serve as a kind of drought index that enables practitioners to quantify the relationship between drought and sinkhole occurrence by remote sensing products of IMERG based precipitation and GRACE-based groundwater storage. The results show that SCDI distribution with high index values is in line with the sinkholes occurred mainly after 2010. image