Evaluation of spatial and temporal relationships between large‐scale atmospheric oscillations and meteorological drought indexes in Turkey

Abstract

Spatial and temporal linkages between large-scale atmospheric oscillations, namely, North Atlantic Oscillation (NAO), Southern Oscillation (SO) and North Sea-Caspian Pattern (NCP), and meteorological droughts in Turkey were investigated in this study. The corresponding oscillation indexes (NAOI, SOI and NCPI) were considered as monthly time indicators of the oscillations while the Standard Precipitation Index (SPI) obtained from 148 stations was used to define meteorological droughts. The suitability of various probability distributions was evaluated to obtain precise estimations of SPI values. Correlation analyses were then conducted to assess spatial and temporal distribution of the relationship between the oscillation and drought indexes. The linkages between the NAOI, NCPI and SPI were found to be more significant at the lag-0, while the SOI had significant positive and negative correlations with SPI series at lag-1-2. Moreover, our results revealed that the NAO is more dominant in the west and in the central Anatolia regions while the NCP has more influence on the northern and eastern regions.

Decreasing flood hazard evaluated in Turkey using nonstationary models

“…Association between oscillation indices and the AIMF series of station 2122, 2164 and 2149 were generally weak and insignificant, suggesting these oscillations are not mainly responsible for the variability in the annual maximum flood process. Only for station 2154, the NAO showed significant correlation with the AIMF series, confirming previous findings that the NAOI generally shows a weak relationship with Turkish hydro‐meteorological data in southeastern Turkey (Tosunoglu et al, 2018). The physical covariates were included in the fitting of GAMLSS models (as well as time) at the selected stations as shown in Table 5.…”

“…The corresponding indicators for these atmospheric oscillations were obtained for the study period from the Climate Prediction Center (http://www.cpc.ncep.noaa.gov/). Detailed information of these oscillations and their indicators can be found in (Baltaci, Akkoyunlu, & Tayanc, 2018; Mellado‐Cano, Barriopedro, Garcia‐Herrera, Trigo, & Hernandez, 2019; Tosunoglu, Can, & Kahya, 2018).…”

Climate change impacts on meteorological drought using SPI and SPEI: case study of Ankara, Turkey

“…To date, a large number of studies have been conducted indicating appropriate drought indices for different case study regions (e.g. Jiang et al 2015, Meresa et al 2016, Hesami Afsar 2016, Rhee and Yang 2018, Tosunoglu et al 2018, Myronidis et al 2018). If one looks at the index referred to as 'integrated surface drought index' (Wu et al 2013), the data inputs for model construction are grouped under three main categories, namely:…”

Monthly Streamflow Forecasting Using Machine Learning

“…Q(t-1), Q(t-2) and Q(t-3)). To guarantee the final quality of the streamflow series, we checked the homogeneity of the data series using the standard normal homogeneity test (SNHT) and Pettitt tests, which are the commonly used statistical method for evaluation change point (abrupt changes) in the hydro-meteorological data series (Tosunoglu et al, 2018). These homogeneity analyses were performed on the annual mean streamflow data.…”