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Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano

An Author Correction to this article was published on 22 May 2020

This article has been updated

Abstract

The May 2018 rift intrusion and eruption of Kīlauea Volcano, Hawai‘i, represented one of its most extraordinary eruptive sequences in at least 200 years, yet the trigger mechanism remains elusive1. The event was preceded by several months of anomalously high precipitation. It has been proposed that rainfall can modulate shallow volcanic activity2,3, but it remains unknown whether it can have impacts at the greater depths associated with magma transport. Here we show that immediately before and during the eruption, infiltration of rainfall into Kīlauea Volcano’s subsurface increased pore pressure at depths of 1 to 3 kilometres by 0.1 to 1 kilopascals, to its highest pressure in almost 50 years. We propose that weakening and mechanical failure of the edifice was driven by changes in pore pressure within the rift zone, prompting opportunistic dyke intrusion and ultimately facilitating the eruption. A precipitation-induced eruption trigger is consistent with the lack of substantial precursory summit inflation, showing that this intrusion—unlike others—was not caused by the forceful intrusion of new magma into the rift zone. Moreover, statistical analysis of historic eruption occurrence suggests that rainfall patterns contribute substantially to the timing and frequency of Kīlauea’s eruptions and intrusions. Thus, volcanic activity can be modulated by extreme rainfall triggering edifice rock failure—a factor that should be considered when assessing volcanic hazards. Notably, the increasingly extreme weather patterns associated with ongoing anthropogenic climate change could increase the potential for rainfall-triggered volcanic phenomena worldwide.

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Fig. 1: Pre-eruption ground deformation of the study site.
Fig. 2: Rainfall over Kīlauea.
Fig. 3: Diffusion model metadata.
Fig. 4: Pore-pressure change in response to infiltration into Kīlauea’s edifice.

Data availability

Satellite-derived rainfall data (TRMM and GPM satellite data) are available from NASA’s EarthData GES DISC portal (https://doi.org/10.5067/TRMM/TMPA/3H/7). Rainfall gauge data are available from the NOAA’s National Centers for Environmental Information climate data portal (https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USC00511303/detail). Vertical GPS data are available from the Nevada Geodetic Laboratory (http://geodesy.unr.edu/NGLStationPages/stations/; stations CRIM, AHUP, MKAI and KTPM). Additional datasets generated here are available from the corresponding author on reasonable request. Sentinel-1 ascending- and descending-track SAR acquisitions were obtained through Unavco’s Seamless SAR Archive (https://github.com/bakerunavco/SSARA). Vertical displacement (velocity) maps of Kīlauea for the time periods 2014–2017 and 2018 are available at https://doi.org/10.5281/zenodo.3459589, alongside the Shuttle Radar Tomography Mission (SRTM) digital elevation model used for plotting data.

Code availability

An archived version of the code required for data access, analysis and display is available at https://doi.org/10.5281/zenodo.3635944. Python code is in a Jupyter Notebook. Version updates, if applicable, will be made available via GitHub: https://github.com/jifarquharson/Farquharson_Amelung_2020_Kilauea-Nature and https://github.com/geodesymiami/papers/tree/master/Farquharson_Amelung_2020_Kilauea-Nature.

Change history

  • 22 May 2020

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

This study would not have been possible without the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement Mission (GPM)—joint missions between the National Aeronautics and Space Administration (NASA) and the Japanese Space Exploration Agency (JAXA) —and the European Space Agency’s (ESA) Copernicus Sentinel-1 data. Copernicus Sentinel-1 data with six-day imagery are available thanks to the Group on Earth Observation’s (GEO) Geohazard Supersites and Natural Laboratory Initiative (GSNL). This work was supported by funding from the NASA’s Interdisciplinary Research in Earth Science (IDS) program (grant number 80NSSC17K0028 P00003). Data processing was conducted at the High Performance Computing core of the University of Miami’s Center for Computational Science (CCS) using the public domain ISCE and SSARA softwares of the Jet Propulsion Laboratory (JPL) and Unavco, respectively. This study was motivated by preliminary analysis by F. Albino, and has benefited from discussions with I. Johanson, K. Anderson and D. Swanson, as well as the comments of three reviewers. Y. Zhang is thanked for his work in the development of MintPy (https://github.com/insarlab/MintPy). We thank NASA, the Nevada Geodetic Laboratory and the NOAA for making the data used herein freely available, as well as the developers of SSARA, ISCE and MintPy for providing free open-source software. We acknowledge Hawai‘i as an indigenous space whose original people are today identified as Native Hawaiians.

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J.I.F. and F.A. conceived the study. J.I.F. performed the modelling and wrote the initial draft of the manuscript. F.A. processed the InSAR data. Both authors contributed to the discussion and interpretation of the results, and the writing of the paper.

Corresponding author

Correspondence to Jamie I. Farquharson.

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Peer review information Nature thanks Michael Manga and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Comparison of recorded head change and modelled pressure change.

a, Rainfall data obtained from the Hawai‘ian Beaches rain gauge (refer to Fig. 1a inset for location). b, Data from the Paradise Park well (see Fig. 1a inset for the location of the well), digitized from ref. 51 over the time period October 1992–June 1993. c, Pressure evolution at a depth of 1 km modelled using rain data from a. Grey bars highlight peaks evident in well-level data that are echoed in the modelled data on pore-pressure change. Note that well level serves as a proxy for pressure change, dependent on well depth and bore, inertia, storage capacity, tidal effects and atmospheric pressure: these factors are not considered in this illustrative example.

Extended Data Fig. 2 Diffusion model results.

a, Pore-pressure change at 3 km below the surface (1.8 km b.s.l.) modelled over the period January 1950 to April 2019 for model Ω1 (data shown since the 1975 Kalapana earthquake). Data modelled using gauge data are shown in red; data modelled using satellite data are in blue. Four-year averages (dashed lines) are also shown. Vertical bars indicate reported intrusion events within the rift zone. K shows the Kalapana earthquake; P represents the onset of the Pu‘u ‘Ō‘ō eruption; E highlights the 2018 fissure eruption. The arrow indicates the highest modelled pressure change. b, As for a, but for model Ω2, a theoretical high-diffusivity end-member scenario. c, As for a, but for model Ω3, a theoretical low-diffusivity end-member scenario. d, PDF of modelled pressure change at depths 1–6 km below the surface from model Ω1. Arrows highlight the pore-pressure front diffusing from near the surface (1 km) to greater depths over time (months and dates are shown). e, As for d, but for model Ω2 (pressure maxima not shown). f, As for d, but for model Ω3 (pressure maxima not shown).

Extended Data Fig. 3 Predicted binomial distribution of ‘wet’ season eruptions at Kīlauea.

The anticipated means, \(\bar{x}\), and standard deviations, ς, are shown. The observed number of historical ‘wet’ season eruptions (37) is highlighted, with a probability of 0.02. Data are also shown for historical eruptions of VEI 2 and greater.

Extended Data Table 1 Parameters of models shown in Fig. 3

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Farquharson, J.I., Amelung, F. Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano. Nature 580, 491–495 (2020). https://doi.org/10.1038/s41586-020-2172-5

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