Data Analysis in Nonstationary State

Alashan, Sadık

doi:10.1007/s11269-018-1928-2

Cited by 13 publications

(4 citation statements)

References 15 publications

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“…A suggestion for improvement, which has arisen from the analysis of the results, is to add a method that allows us to segregate the trends into categories since the Mann-Kendall test only allows us to say whether or not there are statistically significant trends. Sen [90] developed a procedure that allows the segregating or assessing of the trend by categories into "very low", "low", "medium", "high", and "very high" [91][92][93]. The second aspect refers to the parameters that were analysed.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Effect of Climate Change on the Characteristics of Rainfall in Igeldo-Gipuzkoa (Spain)

López

Ayuso-Muñoz

Goñi

et al. 2023

Water

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In recent years, numerous studies have been carried out on changes in the temperature and precipitation regimes and in the frequency of the extreme events that are a result of climate change. While there is evidence of an increase in temperature at a global level, this globality does not seem to occur with precipitation. The Igeldo weather station (San Sebastian) has one of the longest recorded rainfall intensity series in Spain and can be considered as representative of the Cantabrian coast. This circumstance makes it the ideal place to analyse the trend of the pluviometric regime of this area, and this was precisely the objective of this study. A total of 165 series of pluviometric parameters were obtained to characterise the pluviometric regime. The Mann–Kendall and Spearman tests were applied to evaluate the trends of the different parameters, and the Pettitt test was applied to detect the existence of change points. In all the series, it was proven that there were no significant trends or change points. Significant increasing trends were only detected in the series of maximum winter precipitation. In general terms, it can be concluded that the precipitation regime in Igeldo is quite stationary in the context of climate change.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Effect of Climate Change on the Characteristics of Rainfall in Igeldo-Gipuzkoa (Spain)

López

Ayuso-Muñoz

Goñi

et al. 2023

Water

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“…Non-monotonic trends cannot be detected by classical trend methods because there are different effects climatic impacts on low and high hydro-meteorological variables. Alashan, (2018) proposes non-stationary cumulative functions to forecast future hydrometeorological events effectively. Generally, non-monotonic trends do not change total amount of hydro-meteorological variables.…”

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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“…These studies have indicated the potential non-stationarity of extreme precipitation at daily and sub-daily scales in the US region (Carvalho, 2020; Kirchmeier-Young and Zhang, 2020; Coelho et al, 2022). Currently, the prevailing research approach focuses on assessing the nonstationarity of extreme precipitation through changes in statistical parameters Alashan, 2018;Zhang et al, 2023). However, it is essential to note that the non-stationarity of extreme hydrological sequences fundamentally refers to the deviation from the assumption of a homogeneous distribution.…”

Section: Introductionmentioning

confidence: 99%

Research on the Degree of Non-Stationarity in Extreme Precipitation in the Continental United States

Shao,

Wang,

Wang

et al. 2023

Water Resour Manage

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Identifying the non-stationarity of hydrological extreme value sequences is crucial for understanding the patterns of hydrological systems and conducting reliable risk assessments. Current research on detecting non-stationarity primarily focuses on determining whether the parameters of a sequence vary through statistical tests. In this study, we introduce the concept of distributional changes in the detection of non-stationarity and propose the Cumulative Distribution Function Change Index (CDCI) as a measure to quantify the degree of non-stationarity in extreme value sequences. To demonstrate the feasibility and effectiveness of CDCI in assessing non-stationarity, we apply it to hourly precipitation data from 102 meteorological stations in the United States for the period from 1975 to 2021. We compare CDCI with existing methods for detecting nonstationarity to validate its rationality. Additionally, we explore the relationship between distribution, non-stationarity, and return periods using CDCI. The ndings of this research indicate that: (1) distributional changes can re ect the non-stationarity of the sequence, and CDCI is more sensitive in identifying non-stationarity compared to conventional methods; (2) by combining CDCI with changes in return periods, we can identify the response of the distribution to non-stationarity, including changes occurring predominantly in low or high percentiles, or changes occurring across all percentiles and their magnitude. Therefore, the results not only demonstrate the e cacy and validity of measuring the non-stationarity of hydrological extreme value sequences through distributional changes but also reveal possible relationships among non-stationarity, distributional changes, and changes in return periods.

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Data Analysis in Nonstationary State

Cited by 13 publications

References 15 publications

Analysis of the Effect of Climate Change on the Characteristics of Rainfall in Igeldo-Gipuzkoa (Spain)

Analysis of the Effect of Climate Change on the Characteristics of Rainfall in Igeldo-Gipuzkoa (Spain)

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

Research on the Degree of Non-Stationarity in Extreme Precipitation in the Continental United States

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