An integrated modelling framework for exploring the root causes of flood and drought risk amplification by climate change

Recent disasters have highlighted a concerning possibility: the escalation of climate extremes leading to megafloods and megadroughts, commonly known as "black swans". This phenomenon, called "flood and drought risk amplification", is giving rise to impactful disasters at an increasing frequency, which is beyond our current understanding and modelling capabilities. The lack of comprehension poses a scientific challenge in pinpointing the drivers behind these amplifications.

This scenario prompts a crucial research question about the unexpected increase in flood and drought risks due to climate variability — why, where, and when it may occur. In response, this work aims to create a new modelling framework capable of unravelling the complex interplay of processes and factors contributing to flood and drought risk amplification.

Grounded in the hypothesis that local conditions play a pivotal role in risk amplification, this study focuses on identifying specific elements, known as "leverage points", where a small change or action can significantly impact the investigated system. These leverage points can be quantitatively analysed and classified based e.g. on the level of complexity of the implementation of such changes and their potential for sustainability transformation. To achieve this, this work adopts an integrated modelling approach, combining System Dynamics (SD) modelling with elements from Graph Theory and stochastic methods. SD modelling, increasingly applied in water resources planning and management, supports the mapping of the system’s feedback structure and has the potential to describe and analyse its complexity. As SD models can be represented as a directed graph of variables and their connections, Centrality Measures (e.g., degree centrality, eingenvector centrality, etc.) based on Graph Theory can help quickly and objectively pinpoint important mechanisms regardless of the size or complexity of the map. In addition, to handle uncertainty arising from the incomplete understanding of processes that contribute to risk amplification (especially the counterintuitive ones), the recent concept of Process Based Stochastic modelling will be introduced. This novel approach to uncertainty assessment involves converting the deterministic SD model into a stochastic formulation.

The proposed modelling framework will be applied to different case studies in Europe and other continents, relevant for unexplained flood and drought risk amplification, at regional and local scales. To compensate for the absence of quantitative data on some technical and non-technical factors, local stakeholders will be actively involved throughout various stages of the modelling process. Their engagement not only supplements the data gap but also aids modellers in identifying critical system components, feedback loops, and vulnerabilities.