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

Abstract

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-year and 10-year sub-periods, respectively. The clustering structures exhibited indicated 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-year 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.

Clustering and assessment of precipitation climates using satellite precipitation products and improved machine learning precipitation models

“…K-means method has shown a reliable performance in precipitation clustering in previous studies (Kömüşcü et al, 2022;Pamuji & Rongtao, 2020;Roushangar & Alizadeh, 2018). In addition, the K-means is fast enough to work with large input datasets such as gridded satellite precipitation estimates as evidenced by recent studies (Jin et al, 2021;Pamuji & Rongtao, 2020).…”

“…In this study, the clustering of precipitation climates means identifying separate areas with homogenous precipitation characteristics. In the previous studies, the concept of classifying climates, regimes or regions using the variable of precipitation has been investigated (Darand & Mansouri Daneshvar, 2014;Kömüşcü et al, 2022). Many studies used gauge-based precipitation retrievals to identify precipitation regimes in different areas (Hsu & Li, 2010;Modarres, 2006).…”

Analyzing Wintertime Extreme Winds over Türkiye and Their Relationships with Synoptic Patterns Using Cluster Analysis

“…Western, central, and southern regions that have a dry summer subtropical Mediterranean climate experienced less rain than northern and eastern regions. Kömüşcü et al [31] investigated the spatiotemporal variability of precipitation clusters in Türkiye through the application of k-means clustering. During the time span from 1980 to 2020, the analysis identified five distinct groups, each consisting of four to eight sub-periods lasting either 5 or 10 years.…”