Extreme precipitation (rain or/and snow) in upstream areas of the Himalayan region often triggers water and sediment hazards, including flash floods, landslides, debris flow, and river damming. These hazards often interact with the environmental and human systems, resulting in acute and chronic disasters that deleteriously impact the economy, infrastructure, and livelihoods. As cascading and compound hazards become more common in the Himalayan region, a more holistic approach is needed to understand the complex interaction among human, natural, and engineered systems and tackle problems connected to several system constraints. Recent catastrophes like the 2021 Melamchi disaster cannot be attributed to a single cause; rather, they result from several geomorphologic and hydroclimatic factors and physical processes. Individual and interacting dynamics and their cascading and compounding effects occurred in various settings, from high elevations to river valleys along the Melamchi River. This study diagnoses the likely causal effects of hydroclimatic processes of the 2021 Melamchi disaster by integrating space-borne remotely sensed data, hydrologic and hydrodynamic modeling, and machine learning techniques. We have evaluated several scenarios of landslide damming and breaching and analyzed flooding impacts on major settlements across the Melamchi River. Model outputs evaluated using satellite flood imagery, field-based surveys, and published reports were used to understand the characteristics of the 2021 Melamchi disaster. We find that earlier rain episodes had saturated the soil and increased the river’s water level. Therefore, after the outburst of the landslide dam coupled with heavy downpours, a sudden release of huge water had enormous energy to inundate vast areas of flood plains and damaged settlements near riverbanks far downstream up to the Melamchi Bazar. It was not just one extreme event that led to the devastation but a series of non-extreme events. This further reinforces the idea that not just extreme events, but several not-extreme series of events may lead to disaster. Thus, the chances of such a sequence of cascading events must be investigated for risk computation along with extreme events.

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).