Trend Assessment by the Innovative-Şen Method

Dabanlı, İsmail; Şen, Zekâi̇; Yeleğen, Mehmet Öner; Şişman, Eyüp; Selek, Bülent; Güçlü, Yavuz Selim

doi:10.1007/s11269-016-1478-4

Cited by 146 publications

(65 citation statements)

References 28 publications

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“…More variability in hydroclimate has been noticed in recent years due to changes in climate and unsustainable human activities (Ahmed et al, 2018). The changes in variability have changed hydroclimatic extremes like floods and droughts which is a major concern for decision-makers across the world (Sharafati and Zahabiyoun, 2013;2014;Sonali and Nagesh Kumar, 2013;Dabanlı et al, 2016;Güçlü, 2018;Sharafati an Pezeshki, 2019). Among the hydroclimatic disasters, droughts are economically more destructive compared to any other disasters.…”

Section: Introductionmentioning

confidence: 99%

“…Trend analysis is usually conducted to investigate the long-term systematic changes in climatic variables (Wang et al, 2006;Dinpashoh et al, 2011;Sonali and Nagesh Kumar, 2013;Goyal, 2014;Li et al, 2015;Rashid et al, 2015;Zhang et al, 2015;Araghi et al, 2018;Şen, 2018). Different trend detection methods (e.g., parametric, nonparametric and Bayesian) have been developed to assess trends in hydroclimatic time series (Reeves et al, 2007;Khaliq et al, 2009;Sonali and Nagesh Kumar, 2013;Dabanlı et al, 2016) which include Mann-Kendall (MK) test, Sen's slope estimator, Spearman's rho tests, linear regression and innovative trend test (Şen method). Those methods have been widely employed to detect trends in meteorological drought indices (Partal and Kahya, 2006;Liu et al, 2008;Zhai et al, 2010;Tabari et al, 2012;Gocic and Trajkovic, 2013;Zhang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

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Iran has a predominantly arid and semi‐arid climate where the drought hazards and their variability are the crucial concerns for water resources management. This study assesses the characteristics and trends of meteorological droughts features in different climate regions of Iran. Monthly rainfall data are analysed using Standardized Precipitation Index (SPI) to reconstruct meteorological droughts for different time steps (1, 3, 6, 9 and 12). The drought events are decomposed into three features, namely Severity (S), Duration (D) and Peak (P) to assess their spatial variations in different climate regions. The Mann–Kendall (MK) test is used to detect the trends in drought features in different climate regions. The percentage area (PA) of increasing/decreasing trend in drought features is computed to assess the spatial variations of trends in each region. Besides, SPI is compared with Standardized Precipitation Evapotranspiration Index (SPEI) to understand the importance of evapotranspiration on meteorological droughts. The high correlations between SPI and SPEI indicate that the results obtained using SPI would not vary if a similar analysis is conducted using SPEI. The results obtained using SPI reveal that short‐duration droughts (6 months or less) are more frequent and severe in the northwest and the north along the Caspian plains while the longer‐duration droughts (more than 6 months) are more frequent and severe in the south, southeast and southwest. Overall, the northwest region of Iran is found most susceptible to droughts in terms of S, P and D. Moderate (40% < PA < 60%) or significant (PA > 60%) increase in the drought features are observed in all climate regions for shorter SPI (3 months or less) except northwest, while no drought feature is found to increase significantly for SPI‐12. Overall, the higher increase in drought S, P and D are found in the central arid region and at higher altitudes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial assessment of meteorological drought features over different climate regions in Iran

Sharafati

Nabaei

Shahid

2019

Intl Journal of Climatology

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“…The most widely used homogeneity test of trend is the Van Belle and Hughes (van Belle and Hughes 1984). This test has been widely applied in Hydrology (Dabanlı et al 2016;Akinsanola and Ogunjobi 2017;Kahya and Kalayci 2004).…”

Section: Test Of Homogeneity Of Trendsmentioning

confidence: 99%

Spatial and temporal Trend Analysis of Long Term rainfall records in data-poor catchments with missing data, a case study of Lower Shire floodplain in Malawi for the Period 1953–2010

Rustum

Adeloye

Mwale

2017

Preprint

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Abstract. This paper investigated the long-term trends in precipitation from 16 stations located in the lower Shire catchment 15 in Malawi over the period . Annual trend analysis was first considered, and in order to take into account seasonality and serial correlation, the different months of the year are considered. Trend significance was determined using the nonparametric Mann-Kendall (MK) test statistic while the determination of the trends magnitudes was achieved using Sen's slope method. The homogeneity of trends was examined using the Van Belle and Hughes method. The results indicate that annual precipitation has increased, whereas, monthly precipitation revealed an upward trend in wet seasons (November to 20 April) and a downward trend in dry seasons (May to October). The monthly peak trend analysis has shown upward trend in rainy months at all stations.

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“…Mann‐Kendall (MK) trend test is used for trend detection in hydrometeorological records such as temperature, rainfall, evaporation, evapotranspiration, streamflow, and water quality . In addition, Şen_ITA is used by many researchers, which gives successful results to identify trend component in a given time series such as temperature, precipitation, water quality and relative humidity, solar radiation, streamflow, water level, evapotranspiration in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…precipitation, [16][17][18][19] water quality and relative humidity, 20 solar radiation, 21 streamflow, 22 water level, 23 evapotranspiration 24,25 in the literature.…”

mentioning

confidence: 99%

Combination of modified Mann‐Kendall method and Şen innovative trend analysis

Alashan

2020

Engineering Reports

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Mann‐Kendall (MK) trend test is frequently employed as the most familiar trend detection method. Its application requires serial independence of available hydrometeorological time series records. As suggested in the literature, the serial correlation effect can be removed from the given time series by using prewhitening, variance correction or overwhitening processes such as in the modified Mann‐Kendall (MMK) procedure. The PW process may cause some of the current trends to be removed along with the serial correlation. In this study, the MMK method is supported by Şen innovative trend analysis instead of Sen slope estimator (SSE). The MMK method is applied to monthly maximum temperatures of Oxford station in England, for which the data length is large and the moving trend slope values are calculated starting from 1854 for all durations between 1873 and 2017. The MMK_SSE and MMK_ITA methods yield significant increasing trends between 0.0037 and 0.0125°C/year annual slopes for January, March, May, July, August, September, October, November, December, but for February, there is not any significant trend. While MMK_SSE does not give any significant trend for April that has maximum positive kurtosis and skew, but MMK_ITA reflects an increasing trend of 0.0059°C per year.

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Trend Assessment by the Innovative-Şen Method

Cited by 146 publications

References 28 publications

Spatial assessment of meteorological drought features over different climate regions in Iran

Spatial assessment of meteorological drought features over different climate regions in Iran

Spatial and temporal Trend Analysis of Long Term rainfall records in data-poor catchments with missing data, a case study of Lower Shire floodplain in Malawi for the Period 1953–2010

Combination of modified Mann‐Kendall method and Şen innovative trend analysis

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