Trend Assessment by the Innovative-Şen Method
By Ismail Dabanli, Zekâi Sen, Mehmet Öner Yeleğen, E. Şişman, Bülent Selek, Yavuz Selim Güçlü. Water resources management, 2016
197 citations recorded by the source corpus.
Hydro-meteorological time series may include trend components mostly due to climate change since about three to four decades. Trend detection and identification in a better and refined manner are among the major current research topics in water resources domain. Even though different methodologies can be found for trend detection in literature, two well-known procedures are the Mann-Kendall (MK) trend test and recently innovative-Şen trend method, which provides different aspects of the trend. The theoretical basis and application of these two methods are completely different. The MK test gives a holistic monotonic trend without any categorization of the time series into a set of clusters, but the innovative-Şen method is based on cluster and provides categorical trend behavior in a given time series. The main purpose of this paper is to provide important differences between these two approaches and their possible similarities. The applications of the two approaches are given for hydro-meteorological records including relative humidity, temperature, precipitation and runoff from Ergene drainage basin in the north-western part of Turkey. It is observed that although MK trend test does not show significant trend almost in all the cases, the innovative-Şen approach yields trend categorizations as “very low”, “low”, “medium” “high” and “very high”, which should be taken into consideration in future flood (“very high”) and drought (“very low”) studies.
Subfields: Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/c70e91a7777c06a531ed3d37dc3496a3d41224a0
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In Environmental Engineering
In Global and Planetary Change
Citations in the corpus
- An improved version of innovative trend analyses (2018, methodology): “The method has been used in trend identification studies with water quality, evaporation, precipitation, temperature, streamflow, etc., data by different researchers (Sonali and Kumar 2013; Timbadiya et al. 2013; Elouissi et al. 2016; Güçlü et al. 2016; Öztopal and Şen 2016; Dabanlı et al. 2016).”