There are currently no quantitative short-term eruption forecasts based on peer-reviewed and validated models that are operational for New Zealand’s volcanoes. Specific forecasts produced for work-risk assessments are not generally publicised. During a volcanic crisis, eruption forecasts are demanded under high stress and time-restricted conditions. Many forecasting options exist but none are proven as universally viable, with testing and calibration limited to the hindcasting of specific events. Here, we compare the requirements of six methods with currently available data and monitoring capabilities at each of New Zealand’s volcanoes to determine which methods are currently feasible, as well as those options that may be implemented with additional effort or equipment. In New Zealand, the major limiting factor in method selection is the low number of past instrumentally monitored eruptions. This data gap may be filled by carefully selected analogue data from a global volcano set and expert knowledge. Event trees and the failure forecasting method may be set up at most volcanoes with minimal effort, but the latter can only forecast eruption onset time. Expert interpretation is the only method available in New Zealand for any forecast output type.
Volcanic eruptions represent hazards for local communities and infrastructure. Monogenetic volcanoes (usually) erupt only once, and then volcanic activity moves to another location, making quantitative assessment of eruptive hazards challenging. Spatio-temporal patterns in the occurrence of these eruptions may provide valuable information on locations more likely to host future eruptions within monogenetic volcanic fields. While the eruption histories of many stratovolcanoes along the Cameroon Volcanic Line (CVL) are relatively well studied, only fragmentary data exist on the distribution and timing of this region's extensive monogenetic volcanism (scoria cones, tuff rings, maars). Here, we present for the first time a catalog of monogenetic vents on the CVL. These were identified by their characteristic morphologies using field knowledge, the global SRTM Digital Elevation Model (30 m resolution), and satellite imagery. More than ~1100 scoria cones and 50 maars/tuff rings were identified and divided into eight monogenetic volcanic fields based on the visual assessment of clustering and geological information. Spatial analyses show a large range of areal densities between the volcanic fields from >0.2 km−2 to 0.02 km−2 from the southwest towards the northeast. This finding is in general agreement with previous observations, indicating closely spaced and smaller edifices typical of fissure-fed eruptions on the flanks of Bioko and Mt. Cameroon in the southwest, and a more focused plumbing system resulting in larger edifices of lower spatial density towards the northeast. Spatial patterns were smoothed via kernel density estimates (KDE) using the Summed Asymptotic Mean Squared Error (SAMSE) bandwidth estimator, the results of which may provide an uncertainty range for a first-order hazard assessment of vent opening probability along the CVL. Due to the scarce chronological data and the complex structural controls across the region, it was not possible to estimate the number of vents formed during the same eruptive events. Similarly, the percentage of hidden (buried, eroded) vents could not be assessed with any acceptable statistical certainty. Furthermore, the impact of different approaches (convex hull, minimum area rectangle and ellipse, KDE isopaches) to define volcanic field boundaries on the spatial distribution of vents was tested. While the KDE boundary definition appears to reflect the structure of a monogenetic volcanic field better than other approaches, no ideal boundary definition was found. Finally, the dimension of scoria cones (approximated by their basal diameters) across the CVL was contrasted to the specific geodynamic setting. This region presents a complex problem for volcanic hazard analysis that cannot be solved through basic statistical methods and, thus, provides a potential testbed for novel, multi-disciplinary approaches.
Abstract. Long-term multi-hazard and risk assessments are produced by combining many hazard-model simulations, each using a slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well constrained, atmospheric parameters remain problematic unless working on very short timescales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic weather model that takes openly available ERA5-Land data and produces long-term, spatially varying precipitation data that mimic the statistical dimensions of real data. This allows precipitation to be robustly included in hazard-model simulations. A working example is provided using 1981–2020 ERA5-Land data for the Rangitāiki–Tarawera catchment, Te Moana-a-Toi / Bay of Plenty, New Zealand.
Abstract. Long-term hazard and risk assessments are produced by combining many hazard-model simulations, each using slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well-constrained, atmospheric parameters remain problematic unless working on very short-time scales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic catchment-scale weather model that takes openly available ERA5-land data, and produces long-term, spatially varying precipitation data that mimics the statistical dimensions of real-data. This allows precipitation to be robustly included in hazard-model simulations.
Long-term hazard and risk assessments are produced by combining many hazard-model simulations, each based on a slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well-constrained, atmospheric parameters remain problematic unless working on very short-time scales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic weather model that takes openly available ERA5-land data, and produces long-term (e.g., decadal), hourly, spatially varying precipitation data that mimics the statistical dimensions of real-data. Thus, allowing precipitation to be robustly included in hazard-model simulations. The stochastic weather model (SWM) comprises three steps: Data conversion, block construction, and stochastic weather generation. Due to the relative simplicity of the model and exploiting some coding efficiencies in the R package dplyr, 10 years of hourly data can be generated across a 10 by 10 cell grid (~110 km by 110 km) on a standard desktop computer in < 5 seconds.
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