Trend Assessment by the Innovative-Sen Method

Dabanlı İ., Sen Z., Yelegen M. O., Sisman E., Selek B., Güçlü Y. S.

WATER RESOURCES MANAGEMENT, vol.30, no.14, pp.5193-5203, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 30 Issue: 14
  • Publication Date: 2016
  • Doi Number: 10.1007/s11269-016-1478-4
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.5193-5203
  • Keywords: Clustered trend, Climate change, Mann-Kendall, Innovative-Sen, Ergene basin, IDENTIFICATION, MANAGEMENT, STREAMFLOW, SURFACE, SERIES, BASIN
  • Yıldız Technical University Affiliated: Yes


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-Aen 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-Aen 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-Aen 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.