Assessing the informativeness of a coupled surface–subsurface watershed model for understanding debris flow: a hydrological perspective

ABSTRACT

Characterization of debris flow is critical to both risk assessment and hazard mitigation. Recent technologies enable onsite environmental monitoring sensors for geological disaster monitoring. However, the spatiotemporal understanding of debris flow in remote mountainous areas is still limited due to difficulties in observation networks and its complex driving conditions. Here we apply a coupled surface–subsurface hydrological model to examine the characteristics of the water movements near debris flow sites in southwest China. Our approach captured the temporal dynamics of infiltration, redistribution of soil moisture, groundwater storage, and lateral groundwater fluxes. The lateral groundwater flux and groundwater storage were informative indicators in identifying debris flow location. Such informativeness was only effective when hourly dynamics were analysed. Our findings provide new insight into quantifying debris flow susceptibility. This study suggests that the coupled surface–subsurface watershed modelling approach can be informative for preliminary monitoring, planning and management of debris flow.

KEYWORDS:

• debris flow
• PIHM
• hydrological model
• lateral groundwater flow
• bedrock depth
• mudslide

Editor A. Castellarin; Associate Editor T. Heckmann

Editor A. Castellarin; Associate Editor T. Heckmann

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2021YFC3001000), and the Chinese Academy of Sciences (Grant No. ISSAS2411). The authors thank the associate editor and three reviewers for constructive comments that improved this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/02626667.2024.2362303.

Additional information

Funding

This work was supported by the National Key Research and Development Program of China [2021YFC3001000]; and the Chinese Academy of Sciences [ISSAS2411].