The Initiation of Rain-Triggered Lahars

Rain-triggered lahars are a significant secondary hazard at volcanoes where unconsolidated pyroclastic material is exposed to intense rainfall, frequently occurring for years to decades after the initial eruptive activity affects proximal areas or primary hazard zones. Mechanisms of rain-triggered lahar initiation are often inferred from downstream flow observations, whilst rain-triggered lahar risk mitigation typically relies on ground-based flow detection. As a result, increasing knowledge of the physical processes involved in rain-triggered lahar initiation and enhancing the use of underutilised instrumental networks are key areas in the development of lahar risk mitigation techniques. This thesis examined rain-triggered lahar initiation in three primary ways: (i) The field-based examination of factors influencing the nature of the rain-triggered lahar hazard following the April 2015 eruption of Calbuco; (ii) The development of quantitative rainfall simulation experiments examining the effects of grain size distribution and antecedent rainfall; and (iii) The analysis of rainfall data and instrumental lahar records to devise new methods of rain-triggered lahar prediction and forecasting.
Parameters identified at Calbuco as dictating the spatial variability and magnitude of the post-eruption rain-triggered lahar hazard included the volume and grain-size of emplaced pyroclastic material, vegetation coverage, pre-eruption ice and snow cover, topography and rainfall characteristics. Subsequent laboratory-based rainfall simulation experiments featuring man-made tephra beds examined the quantitative effects of factors including the grain-size of surface tephra and antecedent rainfall upon rain-triggered lahar initiation processes. Increased surface runoff was demonstrated during periods of heightened antecedent rainfall and as a result of reduced surface grain size. Reduced surface grain size also induced the formation of surface crusts, further enhancing runoff.
Real-time telemetered rainfall data have been utilised as an effective basis for the creation and development of lahar forecasting models, with peak rainfall intensity acting as the optimal rainfall parameter for predicting lahar occurrence. The demonstrated increased warning times provided by such real-time predictive models illustrates their value both alongside existing lahar detection networks and as an alternative where such resources are unavailable. The incorporation of antecedent rainfall data has been shown to increase model performance, as has the integration of catchment recovery proxies at locations in periods of eruptive quiescence. The probabilistic models developed within this thesis also facilitate the continuous temporal calibration and adjustment of predictions as the databases used to generate lahar forecasts expand and evolve. The quantitative examination of factors driving rain-triggered lahar initiation processes within this thesis and the development of new techniques of lahar forecasting and prediction provide a platform for enhanced lahar risk mitigation. Further research should aim to integrate quantitative lahar magnitude thresholds into the developed lahar forecasting models, facilitating probabilistic inundation modelling and enhanced inter-location lahar comparisons.