Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

Oruc, Sertac

doi:10.1007/s11600-020-00518-w

Cited by 6 publications

(3 citation statements)

References 61 publications

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“…Burić and Doderović, (2021) stated that the climate of Podgorica (Montenegro) is getting more arid but total precipitation has not significant trends. Trends may not be a sign of non-stationarity (Oruc, 2021;Serinaldi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Non-monotonic trend analysis using Mann-Kendall with self-quantiles

Alashan

2023

Preprint

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Recently, climate change makes itself felt at increasing levels due to rising temperatures, irregular precipitation patterns and changing weather events. Although the frequently used Mann-Kendall (MK) method has disadvantages such as needing serial independence, it helps to detect monotonic trends to investigate climate change effects on a given time series. Climate change may have different features on different levels such as the lows and highs of a given time series, leading to non-monotonic trends. Innovative trend analysis (ITA) as an innovative trend analysis method detects non-monotonic trends, which MK cannot. In this study, MK method is improved to detect non-monotonic trends (non-monotonic MK) and applied for Murat River basin, a branch of Euphrates River, precipitation series at Bingöl, Muş, and Ağrı meteorological stations. Although classical MK method cannot detect any trend on the river basin, non-monotonic MK (NMK) method detects two important decreasing (increasing) trends on the low (high) values of Bingöl and Muş (Bingöl) stations. Also, stationarity analysis is applied through the statistical significance level concept for the river basin precipitation series using the NMK method. Bingöl station has a non-stationary precipitation series with a value of 3.07 and 95% confidence level, while Muş station has a remarkable value of 1.58, Ağrı station conserves its stationarity characteristic on the precipitation series. It is hoped that the newly developed NMK method will help to understand the effects of climate change on hydro-meteorological historical records and predict future events for more efficient hydraulic structure designs.

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Section: Introductionmentioning

confidence: 99%

Non-monotonic trend analysis using Mann-Kendall with self-quantiles

Alashan

2023

Preprint

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“…Turkes et al (2020) indicated that the Central Anatolian Region is one of the regions that will experience semi-arid and dry sub-humid conditions in the future while Altın et al 2012 also stated temperature increase in all of the studied stations and decrease in mean rainfall intensity at the twelve stations including Kırşehir in their study. Oruc (2021) also stated that the Central Anatolia Region is expected face fluctuations in terms of precipitation and drought conditions Furthermore, Boyacı and Küçükönder (2021) indicated the potential water stress for the Kırşehir Province for the coming years. For this reason, the Kırşehir province was chosen for the Hurst exponent and DFA analyses.…”

Section: Introductionmentioning

confidence: 99%

Persistence of Rainfall Time Series: Kırşehir Case Study

Oruc¹

2022

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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This study examines the persistence and long-term correlation of monthly and seasonal precipitation time series of the Kırşehir province (Turkey) for the period of 1960-2019, with the widely used Hurst exponent (H) and Detrended Fluctuation Analysis (DFA) methods. Both methods can be used to detect the long-term memory and correlation to be assessed as a reference of predictability. In order to support the study results, Augmented Dickey Fuller (ADF) and Mann-Kendall (MK) tests were applied to the time series under consideration. In some of the precipitation series, the evidence of persistence and long-term correlation was identified. According to the H exponent values, 10 out of 12 months, winter, and autumn seasons (with both simple R/S and corrected R/S methods), and spring and summer seasons (respectively with simple R/S and corrected R/S methods) exhibit long term correlations. On the other hand, according to the DFA scaling exponent values, 4 out of 12 months, winter and autumn seasons reveal long term correlations. When the H exponent and DFA scaling exponent values are compared only four monthly and two seasonal precipitation series are found to be consistent with each other.

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“…The extreme precipitation indices have been widely used to study the temporal variation trend of extreme precipitation (Baidya et al 2008;Lupikasza 2009). The frequency of extreme rainfall events is expected to increase in the future, and neglecting these changes will result in the underestimation of extreme events (Oruc 2021). To investigate the trend of extreme rainfall events, various methods such as MK (Mittchell et al 1966), moving t-test technique Cox-Stuart, and Pettitt's (P) tests have been applied, and nonstationary generalized extreme value models have been developed (Wu et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Singular value decomposition (SVD) based correlation analysis of climatic factors and extreme precipitation in Hunan Province, China, during 1960–2009

Zhu¹,

Zhou

Xiao

et al. 2021

Journal of Water and Climate Change

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A small change in the mean climate may lead to a dramatic change in the frequency and intensity of extreme climate events. In this study, the relationship between mean temperature (MT) and extreme precipitation and the influence from large-scale circulation were investigated in Hunan Province. The correlation between MT and the frequency of extreme precipitation events in different seasons (spring, summer, and autumn) and time periods (1960–2009) was used to obtain pairs of spatial patterns by the singular value decomposition method. The temporal expansion series displayed a consistent trend of temperature and extreme precipitation, and a mutation was observed to occur approximately during the 1980s–1990s. Temperature exhibited a warming trend after the mutation, but the frequency of extreme precipitation events exhibited obvious spatio-temporal variations. The causes of seasonal differences in the frequency of extreme precipitation events were determined by comparing interdecadal changes in three atmospheric circulation factors (850 hPa winds, the entire layer of vapor transportation fluxes and vapor flux divergence) before and after the mutation was revealed.

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Trend and nonstationary relation of extreme rainfall: Central Anatolia, Turkey

Cited by 6 publications

References 61 publications

Non-monotonic trend analysis using Mann-Kendall with self-quantiles

Non-monotonic trend analysis using Mann-Kendall with self-quantiles

Persistence of Rainfall Time Series: Kırşehir Case Study

Singular value decomposition (SVD) based correlation analysis of climatic factors and extreme precipitation in Hunan Province, China, during 1960–2009

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