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Debris floods most commonly occur in steep mountain channels and on their alluvial fans but can also occur on small gravel bed rivers with watershed areas up to several hundred square kilometers. This became obvious during July 2021 and November 2021 debris floods in northwestern Germany and southwestern British Columbia, Canada. We subdivide debris floods into three categories: those triggered by a supra‐critical bed shear stress ratio, form by dilution from debris flows, and resulting from outbreak floods. This trichotomy challenges traditional hazard assessments; debris floods classify as a fluvial process, yet their destructive mechanics are difficult to characterize. Key hazards interact spatially and temporally in debris floods: inundation, scour, sediment transport and deposition and bank erosion. We describe approaches to quantify hazards by systematically accounting for these processes and introduce a novel approach for hazard quantification and mapping in which flow velocity, depth, and presumed fluid density are combined with the annual event frequency for all event scenarios. The derivative "composite hazard maps" are equally valid for debris floods and debris flows. Isolines of bank erosion based on a probabilistic analysis of a physically based model are added to capture the potential of debris floods to abruptly widen their channels. Substantial challenges remain, specifically in the reliable prediction of sediment transport and progressive bank erosion. Our intention is to homogenize debris‐flood hazard assessments and especially mapping methodologies. This could allow for systematic integration with landuse policies assuring consistency among approaches executed by practitioners acting in this field. Plain Language Summary: Debris floods can be highly destructive. We provide a comprehensive methodology to assess debris‐flood hazards with the goal of developing a consistent approach, allowing for better input to quantitative risk assessments and the selection of appropriate mitigation measures. We suggest a method for debris‐flood mapping that combines the recurrence interval of various debris flood scenarios with their impact force and bank erosion to support a multi‐faceted hazard assessment. Key Points: Debris flood assessments are under‐researched and poorly constrained. This contribution attempts to rectify that situationBeyond debris flood inundation, this contribution outlines methods for sediment bulking, sediment transport, and bank erosionA new method for hazard mapping is proposed based on impact force and event frequency. This allows direct comparison of study sites [ABSTRACT FROM AUTHOR]

Flood loss data collection and modeling are not standardized, and previous work has indicated that losses from different flood types (e.g., riverine and groundwater) may follow different driving forces. However, different flood types may occur within a single flood event, which is known as a compound flood event. Therefore, we aimed to identify statistical similarities between loss‐driving factors across flood types and test whether the corresponding losses should be modeled separately. In this study, we used empirical data from 4,418 respondents from four survey campaigns studying households in Germany that experienced flooding. These surveys sought to investigate several features of the impact process (hazard, socioeconomic, preparedness, and building characteristics, as well as flood type). While the level of most of these features differed across flood type subsamples (e.g., degree of preparedness), they did so in a nonregular pattern. A variable selection process indicates that besides hazard and building characteristics, information on property‐level preparedness was also selected as a relevant predictor of the loss ratio. These variables represent information, which is rarely adopted in loss modeling. Models shall be refined with further data collection and other statistical methods. To save costs, data collection efforts should be steered toward the most relevant predictors to enhance data availability and increase the statistical power of results. Understanding that losses from different flood types are driven by different factors is a crucial step toward targeted data collection and model development and will finally clarify conditions that allow us to transfer loss models in space and time. Key Points: Survey data of flood‐affected households show different concurrent flood types, undermining the use of a single‐flood‐type loss modelThirteen variables addressing flood hazard, the building, and property level preparedness are significant predictors of the building loss ratioFlood type‐specific models show varying significance across the predictor variables, indicating a hindrance to model transferability [ABSTRACT FROM AUTHOR]

Flood damage processes are complex and vary between events and regions. State‐of‐the‐art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth‐damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data are available, we use variables pertaining to specific region‐ and event‐characteristics representing commonly available expert knowledge as group‐level predictors within the HBM. Key Points: Hierarchical Bayesian model (HBM) captures spatiotemporal variability in flood damage processes better than established depth‐damage functionsRegion‐ and event‐specific characteristics that explain the variability in damage processes improve spatiotemporal transfer of the HBMThe HBM is tested for transferability using empirical damage data from six events in the Elbe, Danube, Rhine and Oder catchments in Germany [ABSTRACT FROM AUTHOR]