Journal of Water and Climate Change (Oct 2022)

Dynamics of precipitation regions of Turkey: A clustering approach by K-means methodology in respect of climate variability

  • Ali Ümran Kömüşcü,
  • Ertan Turgu,
  • Tracy DeLiberty

DOI
https://doi.org/10.2166/wcc.2022.186
Journal volume & issue
Vol. 13, no. 10
pp. 3578 – 3606

Abstract

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Delineation of homogenous precipitation regions can be rather challenging when considerable spatial variability and seasonality of precipitation exist within a large geographic domain. This study aims to investigate and redefine the spatio-temporal variability of precipitation clusters of Turkey by K-means clustering at different time scales. The monthly precipitation of 234 stations for the 1980–2020 period was included. Five precipitation clusters are identified for the 1980–2020 period, while the clusters varied between four and eight and four and six for the 5- and 10-year sub-periods, respectively. The clustering structures exhibited an indication of certain links to the prominent teleconnection patterns. The monthly precipitation correlated more significantly with Arctic Oscillation (AO) in the Aegean and Mediterranean regions, while North Atlantic Oscillation correlated better with the Eastern and Southeastern Anatolian stations. The variability of the cluster structures is described in association with the coefficient of variation (COV) spatial patterns using the observed station data. At 5- and 10-year subsequent periods, no significant variability in the cluster patterns was observed in the lower COV areas where the monthly precipitation was more uniform throughout the year, while more inter-decadal variability was prominent in the higher COV areas where seasonality in precipitation was more pronounced. Overall, the precipitation regions were not spatially coherent over time, and considerable variability was revealed between different regions of the country. HIGHLIGHTS Using the K-means machine learning technique for clustering homogenous precipitation regions and assessing their temporal evolution.; Associating changes in the spatial structure of the precipitation regions to climate variability, especially to the prominent teleconnection patterns.; Finding a link between the coefficient of variation (COV) and the stability of the precipitation cluster structures.;

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