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https://www.selleckchem.com/products/AZD1152-HQPA.html ead to colder ecoregions driven by climate change.The understanding of compounding and cascading impacts is becoming increasingly central to the risk reduction debate as hazard consequences are strongly coupled. Still, studies on their quantification and visualization are limited. This restricts the establishment of impact-based early warning systems. Here, a novel method for quantifying drought compound impacts and their cascading paths is presented by integrating network analysis and data mining tools. The 2018/19 drought in Germany is used as a case study. Network graphs are employed to display impact co-occurrences and cascades of agriculture, livestock, forestry, industry, and recreation impacts. Furthermore, sequential pattern mining is used to predict the next impact that is likely to take place. A synthesis of the identified relationships is presented using accessible visual formats. Results show that simultaneous and cascading drought impacts may not happen by chance but follow a pattern. Indeed, statistically significant co-occurrence associations outnumbered randomly distributed ones (91.1% versus 8.9%). With regard to the cascading paths, cross-validation results show that within three attempts, the next impact class was accurately predicted in 72.9% of the cases. Crop losses were usually followed up by a shortage of feed for livestock and consequent early slaughtering of animals. This implies that in order to limit drought impacts, there is a need to consider their compounding and cascading effects. Hence, researchers need to move from the analysis of single impacts to the understanding of how multi-sectoral impacts are connected with each other. The methodology proposed here paves towards this direction. The visualization tools used can help to increase awareness of the possible impact interactions and dependency, improving drought managers' decision-making ability. Moreover, the obtained results c

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