Drought impact profiles: Analyzing multivariate socio-economic drought impacts using nonlinear dimensionality reduction

Abstract

<p>Socio-economic drought impacts often occur concomitantly across multiple sectors, leading to more severe consequences than if they affected single sectors. Improved management of such disasters requires cross-sectoral impact assessments and analyses. As such, analyzing how regions are affected by multiple impacts can provide crucial information for mitigating their consequences. Here, we characterize the multivariate distributions of socio-economic drought impacts. Our aim is to understand patterns by which diverse drought impacts co-occur. We introduce the concept of drought impact profiles, which describe characteristic distributions of co-occurring impacts. To this end, we use a unique spatio-temporal dataset generated with text mining and machine learning applied to newspaper articles. This dataset describes reported socio-economic drought impacts along seven categories (agriculture, forestry, fires,  social, aquaculture, livestock, waterways) in Germany between 2000-2022. We combine several dimensionality reduction algorithms (PCA, ISOmap, self-organizing maps) to generate robust and interpretable representations of the drought impacts. Our results show characteristic patterns for both particular drought events and regions. Also, the applied methods provide a low-dimensional representation of the multivariate socio-economic drought impacts. This research provides a methodological contribution to the holistic, empirical investigation of co-occurring drought impacts. The proposed methods can inform risk models, and policy-makers on the urgency of cross-sectoral governance approaches. Also, the proposed method could apply to other hazards or compound events.</p>

Uncovering the Dynamics of Multi‐Sector Impacts of Hydrological Extremes: A Methods Overview

“…By leveraging these methods, researchers can better understand the relationships between multiple socio-economic impacts (Challenge 3). Although dimensionality reduction methods have been successfully applied to identify underlying risk patterns (e.g., hazard, vulnerability) that drive impact occurrence (e.g., Johnson et al, 2020;Maity et al, 2013), their application in the field of CCI is still incipient (e.g., Sodoge, Kuhlicke, Mahecha, et al, 2023). Adopting dimensionality reduction approaches in CCI research holds promise for gaining a comprehensive perspective on the relationships between different multi-sector impacts (Challenge 3) as well as across different regions (Challenge 4).…”