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PhD-Project I9: Analysis of concurrent climate extremes driving land degradation and recovery in the Mediterranean regions

Timescale: Oct.2018 – Sept.2021

Supervisors:

Prof. Dr. Eva Paton, TU Berlin,

Prof. Dr. Axel Bronstert, University of Potsdam,

Dr. V. Aich, WMO

Background:

A combination of severe climate events in the form of low seasonal to annual precipitation occurring together with several high-intensity storms and high temperatures are known to have a particular severe impact on dryland ecosystems and their ecosystem functions, even though individual events involved may not be counted as extremes themselves. In the past, most analyses of climate and weather extremes typically tend to focus on only one of the climatic conditions; however, this univariate approach may underestimate the effects of concurrent and compound extremes on dryland degradation. Multivariate extreme events, i.e. extremes of two or more climate variables occurring simultaneously, are likely to impact dryland ecosystems greater than their univariate counterparts. The succession of these events is also likely to play a critical role on how likely an ecosystem will be able recover from previous detrimental events. However, concurrent, compound and succession quantities of dryland ecosystems are quite unexplored, for past changes as well as for future changes.

 Objectives and Methods

To address this issue, this study will analyse changes in concurrences of several variables, such as meteorological and hydrological droughts, flashfloods due to extreme rainfall, heatwaves and vegetation degradation status for the last 40 years in selected catchments within eastern Mediterranean regions. The study will investigate changes in these concurrent variables using adapted statistical techniques for time series analysis on concurrency. The Kolmogorov–Smirnov test is used to assess differences between the CDFs of the concurrent drought and heatwave events for different time periods; the Mann-Kendall test with a framework based on the Cramér – von Mises change point detection will be employed to evaluate temporal changes in concurrent events. And finally, Markov chains will be employed for the analysis of the dynamics and succession of multivariate or compound extreme events. Analyzing these historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative impacts of climatic change and land degradation. It will allow identifying windows of risks in the past, where an ecological degradation shift was likely to have occurred and windows of opportunity, where an ecosystem recovery shift would be most liable and successful when combined with land-use change adaptation.