Effect of Short-Term and Long-Term Persistence on Identification of Temporal Trends

Dinpashoh, Yagob; Mirabbasi, Rasoul; Jhajharia, Deepak; Abianeh, H Zare; Mostafaeipour, Ali

doi:10.1061/(asce)he.1943-5584.0000819

Cited by 120 publications

(43 citation statements)

References 16 publications

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“…It has been widely used in the time series trend analysis in hydro-meteorological research (Birsan et al 2005;Hamed 2008; Modarres and Sarhadi 2009;Tabari et al 2011;Yue et al 2002). The interested readers can refer to Hamed (2008), Kumar et al (2009), and Jhajharia et al (2012, 2014b and Dinpashoh et al (2014) for the further detailed formula of the MK test. However, the effect of serial correlation is one of the major problems faced during the identification of trends in ET 0 and various meteorological variables.…”

Section: Trend Analysis Methodsmentioning

confidence: 99%

“…Douglas et al (2000) believe that this method will weaken the reliability of the MK method on the time series trend significance. Yue et al (2002) improved this method and proposed a trend-free prewhitening (TFPW) method which is widely applied in related research areas (Yue et al 2002;Kumar et al 2009;Dinpashoh et al 2011Dinpashoh et al , 2014Jhajharia et al 2013Jhajharia et al , 2014aVousoughi et al 2013). The details are as below:…”

Section: Trend Analysis Methodsmentioning

confidence: 99%

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Sensitivity study of reference crop evapotranspiration during growing season in the West Liao River basin, China

Gao

Dong

et al. 2015

Theor Appl Climatol

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We have analyzed the trends of reference crop evapotranspiration (ET 0 ) through the Penman-Monteith model and climate factors in the West Liao River basin using the Mann-Kendall test after removing the effect of significant lag-1 serial correlation from the time series of the data by trendfree pre-whitening. The changing characteristics of the sensitivity coefficients and the spatial distribution during growing season are investigated, and the correlation between the sensitivity coefficients with elevation and the key climate factors by relative contribution and stepwise regression methods are evaluated. A significant overall increase in air temperature, and a significant decrease in wind speed, solar radiation, sunshine duration, relative humidity, and a slight decrease in ET 0 are observed. Sensitivity analysis shows that ET 0 is most sensitive to solar radiation, followed by relative humidity. In contrast, ET 0 is least sensitive to the average air temperature. The sensitivity coefficients for the maximum and minimum air temperature and relative humidity have a significant negative correlation with elevation, while the coefficients for other variables are not strongly correlated with elevation. The spatial distribution of the sensitivity coefficients for wind speed and solar radiation is opposite, i.e., in regions where the sensitivity coefficients for wind speed are high; the sensitivity coefficients for solar radiation are low and vice versa. The sensitivity for relative humidity and average air temperature is region specific in the plain area. However, ET 0 is most sensitive to the climate change in regions of high elevation. Wind speed is the most dominant contributor followed by solar radiation. Average air temperature contributes the least. The stepwise regression analysis indicates that wind speed is the foremost dominant variable influencing ET 0 . Relative contribution and stepwise regression analysis can be used to determine the main variables affecting ET 0 , and it also strongly supports that the aerodynamic component is the dominant factor.

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Section: Trend Analysis Methodsmentioning

confidence: 99%

Section: Trend Analysis Methodsmentioning

confidence: 99%

Sensitivity study of reference crop evapotranspiration during growing season in the West Liao River basin, China

Gao

Dong

et al. 2015

Theor Appl Climatol

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“…Therefore the Mann-Kendall (MK) test, a rank-based nonparametric test for assessing the significance of a trend, has been also used in this study. The nonparametric tests are better than parametric tests as reported by various researchers (Vousoughi et al, 2013;Dinpashoh et al, 2013). The MK test provides a robust test for trend, free from assumptions about mathematical form of the trend or the probability distribution of errors (Hasanean, 2001;Chattopadhyay et al, 2011).…”

Section: Trend Component (T T )mentioning

confidence: 93%

Time Series Modelling of Pan Evaporation-A Case Study in the Northeast India

Dabral¹,

Jhajharia²,

Mishra³

et al. 2014

Global NEST Journal

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In the present study, a monthly time series model was developed for pan evaporation (E pan ) in Jorhat (Assam), northeast India. We assume that the E pan series can be decomposed into deterministic and stochastic components, and a turning point test was used to identify the presence of a trend component in the series. A trend-free E pan time series was further obtained by eliminating the trend component from the original E pan time series, and later used in identifying the periodic component using Fourier series analysis. Results show that the first three harmonics explained the total variance by 88.2 %. Before modelling the stochastic dependent component, the periodic component was removed from the time series. In the stochastic dependent component modelling, the moving average (MA) model of order 2 was found suitable on the basis of minimum value of the Bayesian information criterion (BIC). The independent stochastic component was modelled using the normal distribution function. Overall, a very good agreement is observed between generated and historical records, with the Nash-Sutcliffe coefficient being equal to 0.98.

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“…Haktanir et al (2013) [21] investigated on 48-year-long complete and 79-year-long incomplete maximum daily precipitation series recorded at Alexandria, Egypt, and on 61-year-long maximum daily precipitation series recorded at Antalya. Dinpashoh et al (2014) [22] analysed trends of precipitation by using the MK trend detection test and determined the trends on monthly, seasonal, and annual time scales by using the precipitation data for the period of time 1955-2004 of the 16 stations selected from Iran. Fathian et al (2014) [23] used three non-parametric statistical tests, the MK, Spearman rho, and Sen's T, and applied to estimate the trends in the annual and seasonal time series of temperature, precipitation, and streamflow at 95 stations in Urmia Lake basin, Iran.…”

Section: Introductionmentioning

confidence: 99%

Yozgat’ın Aylık Toplam Yağış ve Aylık Ortalama Hava Sıcaklıklarının Trend Analizi

Ay¹,

Özyildirim²

2017

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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The purpose of this study is to detect possible trends in monthly total rainfall and monthly mean air temperature on the basis of Mann-Kendall (MK) and a new method recently proposed by Şen. The new method is used for trend analysis of both variables' data recorded at Yozgat province in Turkey over the period 1970-2016. For comparison purpose, the well-known MK trend test is also applied to the same data. In the first step, the historical data were divided into two time periods : 1970-1992 (276 values) and 1993-2016 (288 values). Each time part was analysed individually, and both parts were compared with each other. Consequently, there is no statistically significant trend for both variables at 95% two-tailed confidence interval in the period 1970-1992 and 1993-2016

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Effect of Short-Term and Long-Term Persistence on Identification of Temporal Trends

Cited by 120 publications

References 16 publications

Sensitivity study of reference crop evapotranspiration during growing season in the West Liao River basin, China

Sensitivity study of reference crop evapotranspiration during growing season in the West Liao River basin, China

Time Series Modelling of Pan Evaporation-A Case Study in the Northeast India

Yozgat’ın Aylık Toplam Yağış ve Aylık Ortalama Hava Sıcaklıklarının Trend Analizi

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