A worldwide event-based debris flow barrier dam dataset from 1800 to 2023

Abstract. Debris flows, as a special kind of landslide, often block rivers to form barrier dams and trigger a series of disasters such as upstream aggradation and outburst floods. The understanding of debris flow barrier dams (DFBDs) is poor, mostly due to existing studies focusing on individual events and a lack of summarization of multiple DFBD events. The existing global or regional datasets of landslide barrier dams (LDs) contain only a few cases of DFBDs and ignore the differences between DFBDs and other landslide barrier dams (LDs), such as the dams of rock slides, debris avalanches, or earth slides. To fill this gap, we reviewed 2519 high-quality literature and media reports. Focusing on identified debris flow damming events, a rigorous data review and validation process was conducted using Google Earth. A systematic approach was employed to prioritize conflicting information from various data sources. Consequently, a global dataset was compiled, encompassing 555 historical DFBDs from 1800 to 2023. This pioneering global dataset includes 6 categories and 38 attributes, detailing DFBDs. It captures basic information (location, the date of formation, etc.), dam characteristics (height, length, volume, etc.), lake characteristics (area, capacity, length), debris flow characteristics (velocity, discharge, volume, etc.), failure characteristics (peak discharge, loss of life, etc.), and climate characteristics (precipitation and temperature). Our dataset elucidates that DFBDs exhibit key features of instability, complete blockage, and overtopping failure. The number of such dams has notably increased, especially in China. A total of 15 % of channels showed recurrent debris flows, resulting in DFBDs that make up 35 % of all DFBDs. Further analysis suggests that the Ls (AHV) model should be used for priority use, followed by the DBI model, in the stability assessment of DFBDs. Compared to other barrier dam datasets, our dataset is more targeted places greater emphasis on the review of raw data, and stresses the unification of terminology and concepts (i.e., blockage modes and stability), ensuring the consistency and accuracy of the data. The dataset and results in this work may help to deepen the understanding of DFBD formation, distribution, and evolution. The DFBD dataset can be accessed through this link: https://doi.org/10.5281/zenodo.14766647 (Cheng et al., 2025).