Deformation-informed hydrometeorological thresholds for landslide early warning: inventory enhancement and spatiotemporal downscaling

Hydrometeorological thresholds are central to many operational landslide early warning systems, yet they often remain coarse and weakly linked to slope physics. Two persistent limitations are (i) the dependence on landslide inventories that are incomplete and often poorly timed, and (ii) the assumption that a single regional threshold can represent heterogeneous and evolving slope stability conditions. This contribution presents a deformation-informed perspective to advance threshold-based landslide early warning. We show that slope deformation, measured as continuous time series, can act as an transition state variable that bridges hydrometeorological forcing and slope failure. By explicitly incorporating deformation, hydrometeorological thresholds can be better constrained as well as better used operationally. First, deformation observations can be used to supplement event information for threshold assessment. Automated extraction of “deformation events” from geodetic time series can complement landslide records as physically meaningful proxies, reducing the sensitivity of threshold estimation to inventory incompleteness and timing uncertainty, and improving the robustness of calibrated thresholds. Second, deformation can guide the spatiotemporal refinement of warning criteria. By quantifying how different slopes respond to rainfall over multiple time windows, deformation-derived indices can characterize slope-specific response patterns and stability states. This information enables a downscaling strategy in which regional hydrometeorological thresholds for landslide initiation are transformed into slope-specific, dynamically updated thresholds that better reflect local conditions and temporal changes in stability. In this way, deformation moves thresholds from static and regionally averaged triggers toward adaptive criteria that are more physically grounded and spatially actionable.

Overall, the proposed deformation-aware framework brings two complementary benefits in early warning: (1) strengthening landslide initiation threshold development through deformation-informed event characterization, and (2) enhancing threshold application through slope-specific, time-varying adaptation. This approach is sensor-agnostic (applicable to GNSS and InSAR) and compatible with different threshold formulations, offering a practical pathway to improve reliability and reduce uncertainty in landslide early warning across data-limited and highly heterogeneous regions.