Multivariate Analysis of Land-Atmosphere Interactions in a Changing Climate

• Applicant: Professor Dr. Rene Orth
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 391059971

Human-made greenhouse gas emissions are known to affect the global climate. These changes will also propagate to influence the water cycle, for example directly through changes in precipitation, or indirectly through changes in evaporative demand as a result of changes in temperature. As a result, this can lead to more and/or amplified droughts. Droughts have major implications on ecosystems and society, such that changes in drought dynamics present a major challenge for the management of, and adaptation to, such aggravated water extremes. This requires an accurate characterization of agricultural droughts and their effects on the large-scale water cycle, which is the focus of this proposal.In particular, we will derive an machine learning-based runoff dataset, which can subsequently be employed in conjunction with machine learning-based soil moisture and evapotranspiration datasets to resolve the global water cycle response to drought. The runoff modeling will further be used to identify and characterize drought legacy effects. This will be done by comparing observed runoff with the runoff simulated by machine learning trained without the time periods after droughts such that the algorithm can not represent legacy effects. Finally, we will reveal the propagation of drought-related water deficits into runoff and evapotranspiration across the globe and determine the relevance of potential drivers such as soil and vegetation characteristics or the climate regimes. Furthermore, we will investigate the performance of physically-based land surface models to represent these drought impact patterns. The proposed work builds upon, and benefits from, previous work within the first part of this Emmy Noether project where (i) the machine learning methodology was successfully developed and applied for soil moisture data (O and Orth 2021), and (ii) the water cycle response to drought has been studied across Europe (Orth and Destouni 2018). This way, the experience and tools acquired in these past studies will leverage and benefit the analyses and scientific outcomes of this renewal project.

• DFG Programme: Independent Junior Research Groups