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Most regional landslide warning systems utilize empirically derived rainfall thresholds that are difficult to improve without recalibration to additional landslide events. To address this limitation, we explored the use of synthetic rainfall to generate thousands of possible storm patterns and coupled them with a physics‐based hydrology and slope stability model for various antecedent soil saturation scenarios to analyze pore water pressure and factor of safety metrics. We used these metrics to generate two‐tiered alert thresholds that can be employed to assess shallow landslide potential for any given combination of storm and antecedent wetness. When applied to the San Francisco Bay region (CA, USA), the results are consistent with events that caused widespread landsliding. Our deterministic modeling approach, which accounts for plausible ranges in soil hydraulic and mechanical properties, can inform the development of the next generation of warning systems for rainfall‐induced landsliding. Plain Language Summary: Providing accurate warning in advance of rainfall‐induced landsliding can help reduce loss of life and property but requires knowledge of hillslope wetness and future rainfall conditions to forecast when widespread landsliding is likely. However, typical landslide warning systems consider only the rainfall characteristics (e.g., rainfall amount, duration, and/or intensity) that were measured during past landslide events. These are used to define a set of rainfall conditions, or a threshold, for which landsliding should be expected within a given area. When a rainfall threshold does not perform well for subsequent landsliding events, it can be difficult to diagnose the problem because hillslope wetness is not considered. We present an approach to develop landslide thresholds that accounts for both rainfall and hillslope wetness. The results are consistent with historical and recent widespread landsliding events in our study region and, more importantly, can be related to measurable properties of hillslopes. This more direct approach could revolutionize the development of the next generation of landslide warning systems. Key Points: Thresholds for rainfall‐induced landsliding of soil‐mantled hillslopes identified with physics‐based hydrologic and slope stability modelingLibrary of modeled infiltration events based on variations in rainfall and antecedent soil saturation facilitates timely hazard assessmentDeterministic modeling approaches that account for parameter variability can inform the next generation of landslide warning systems [ABSTRACT FROM AUTHOR]

This work focuses on the development of a combined statistical-mechanical approach to predict changes in landslide displacement rates from observed changes in rainfall amounts. The forecasting tool FLAME (Forecasting Landslides Accelerations induced by Meteorological Events) associates (1) a statistical impulse response (IR) model to simulate the changes in landslide rates by computing a transfer function between the input signal (e.g. rainfall) and the output signal (e.g. displacement) and (2) a simple 1D mechanical (MA) model (e.g. viscoplastic rheology) to take into account changes in pore water pressure. The models have been applied to forecast the displacement rates at the Super-Sauze landslide (South East France). The performance of different combinations of models (IR model alone, MA model alone and a combination of the IR and MA models) is evaluated against observed changes in pore water pressures and displacement rates at the study site. Results indicate that the three models are able to reproduce the displacement pattern in the general kinematic regime (succession of acceleration and deceleration phases); conversely, extreme kinematic regimes such as fluidization of part of the landslide mass are not being reproduced. The approach constitutes however a robust tool to predict changes in displacement rates from rainfall or groundwater time series. [ABSTRACT FROM AUTHOR]