Abstract. Debris flows are natural disasters that frequently occur in
mountainous areas, usually accompanied by serious loss of lives and properties.
One of the most commonly used approaches to mitigate the risk associated with debris
flows is the implementation of early warning systems based on well-calibrated
rainfall thresholds. However, many mountainous areas have little data
regarding rainfall and hazards, especially in debris-flow-forming regions.
Therefore, the traditional statistical analysis method that determines the
empirical relationship between rainstorms and debris flow events cannot be
effectively used to calculate reliable rainfall thresholds in these areas.
After the severe Wenchuan earthquake, there were plenty of deposits deposited
in the gullies, which resulted in several debris flow events. The
triggering rainfall threshold has decreased obviously. To get a reliable and
accurate rainfall threshold and improve the accuracy of debris flow early
warning, this paper developed a quantitative method, which is suitable for debris
flow triggering mechanisms in meizoseismal areas, to identify rainfall
threshold for debris flow early warning in areas with a scarcity of data based
on the initiation mechanism of hydraulic-driven debris flow. First, we
studied the characteristics of the study area, including meteorology,
hydrology, topography and physical characteristics of the loose solid
materials. Then, the rainfall threshold was calculated by the initiation
mechanism of the hydraulic debris flow. The comparison with other models and
with alternate configurations demonstrates that the proposed rainfall
threshold curve is a function of the antecedent precipitation index
(API) and 1 h rainfall. To test the proposed method, we selected the
Guojuanyan gully, a typical debris flow valley that during the 2008–2013
period experienced several debris flow events, located in the
meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison
with other threshold models and configurations shows that the selected
approach is the most promising starting point for further
studies on debris flow early warning systems in areas with a scarcity of data.