IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Clustering for hydroclimatic extremes: a retrospective synthesis of the literature

Nilay Dogulu1, Manuela Irene Brunner2, Svenja Fischer3, and Wouter Knoben4
Nilay Dogulu et al.
  • 1Independent researcher, Ankara, Turkey (nilay.dogulu@gmail.com)
  • 2Institute of Earth and Environmental Sciences, University of Freiburg, Freiburg, Germany (manuela.brunner@hydrology.uni-freiburg.de)
  • 3Hydrology, Water Management and Environmental Engineering, Ruhr-University Bochum, Germany (svenja.fischer@rub.de)
  • 4Centre for Hydrology, University of Saskatchewan, Canmore, Alberta, Canada (wouter.knoben@usask.ca)

Earth system processes have complex physics and are dynamically interlinked, making modelling and predictions difficult. In particular, current challenges for hydroclimatic systems are in understanding nonstationarity and heterogeneity driven by climatic and human influences. Hence, studying the spatial and temporal occurrences and dependencies of hydroclimatic extremes is becoming increasingly important for water resources management and hydrological services. 

Research efforts that address these issues for extreme hydrological events are large and diverse in their approaches and methodologies. In this respect, multivariate statistical methods such as clustering are approaches commonly used to reveal mechanisms affecting floods and droughts in relation to their trends and magnitude as well as their variability in time and space. Clustering is a convenient tool to analyze large hydrometeorological datasets because of its unsupervised nature. However, there are no structured insights for hydrologists to reflect on the principles and findings of data clustering for hydroclimatic extremes. 

This contribution sheds light on the why’s and how’s of clustering methods for floods and droughts based on a systematic review of the literature. Our aim is to provide a synthesis of hydrological themes and methodological concepts found in papers that investigate floods and droughts through data clustering. These insights are valuable for guiding future applications of clustering methods while enabling wider discussions on the knowledge gaps for modelling extremes in hydroclimatic systems.

How to cite: Dogulu, N., Brunner, M. I., Fischer, S., and Knoben, W.: Clustering for hydroclimatic extremes: a retrospective synthesis of the literature, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-262, https://doi.org/10.5194/iahs2022-262, 2022.