The aim of alarm flood detection is the identification of similar, frequently occurring sequences of alarm messages in historical alarm data and uses the results for root cause analysis or alarm flood reduction. Various promising approaches for alarm data of automated production systems exist. However, due to the high amount of alarm messages transmitted by industrial alarm systems, floods are often interrupted by alarms stemming from different root causes, leading to non-relevant or invalid results of purely data-driven flood detection approaches. To improve the results of data-driven approaches, this paper suggests considering a process plant's hierarchy to divide historical alarm data into independent sub-datasets. For this reason, the paper discusses necessary plant information to explain a process plant's hierarchy and analyzes existing approaches to extract this hierarchy automatically from information sources. It then discusses whether existing approaches for alarm flood detection consider this hierarchy and how it could improve the approaches' results.