A Thermal Record for Unrest at Vulcano 2020–2022: In Situ Meteorological Data and Soil Temperature Recorded at High Temporal Resolution

Pailot-Bonnétat, Sophie; Harris, Andrew J. L.

doi:10.1007/s00445-023-01696-3

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…However, even the most intense, cloudburst events did not force abnormality on the surface temperature time series, meaning that when the system is at baseline levels, surface temperature normality is immune to rain. However, this conclusion applies only to surface temperatures (Pailot-Bonnétat et al, 2023;Pailot-Bonnétat and Harris, 2024).…”

Section: Figure 13mentioning

confidence: 98%

“…The network consists of two temperature stations separated by 50 m, with one inside the heated zone and one in the nonheated zone, plus a weather station co-located with the latter station (Figure 2C). The experimental set-up, sensors and data sets used here are fully described in Pailot-Bonnétat and Harris (2024), and are summarized here in Table 1. The two temperature stations consisted of two thermocouples (Onset HOBO TMC1-HD, signal to noise ratio = 0.1°C) measuring surface temperature (T s ) and air temperature (T a ) at a height of 15 cm above the surface.…”

Section: Background and Datamentioning

confidence: 99%

“…To do this we apply the deep learning framework DITAN (Giannoulis et al, 2023) to time series data for surface temperature and meteorological conditions collected in-situ at the active hydrothermal system at Vulcano (Pailot-Bonnétat and Harris, 2024), in an experiment designed to answer the stated research question. We begin by describing the characteristics of the study site (Vulcano) and the instrumentation installed there, before reviewing DITAN, its performance and output.…”

Section: Introductionmentioning

confidence: 99%

“…We begin by describing the characteristics of the study site (Vulcano) and the instrumentation installed there, before reviewing DITAN, its performance and output. In effect, this is the third in a three part series of papers where we have first set-up, tested and crossvalidated DITAN in Giannoulis et al (2023), and then (second) set up the experiment and data collection design in Pailot-Bonnétat and Harris (2024).…”

Section: Introductionmentioning

confidence: 99%

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External factors driving surface temperature changes above geothermal systems: answers from deep learning

Giannoulis,

Pailot-Bonnétat,

Barra

et al. 2024

Front. Earth Sci.

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Introduction: The surface expression of enhanced geothermal heat fluxes above an active hydrothermal system causes a surface thermal anomaly (ΔT). The thermal anomaly is expressed by the difference between the temperature within the heated zone (Th) and the temperature of non-heated surfaces (T0). Given that the resulting thermal anomaly at the surface is of extremely low magnitude (1°C–5°C at Vulcano, Italy), it is extremely sensitive to overprinting by external factors, namely, meteorological influences on surface temperature variation, such as solar heating, wind and rain.Methods: To test the sensitivity of the surface to external drivers, we installed two surface temperature measurement stations within the Vulcano’s Fossa crater, one inside the thermal anomaly and one outside (separation = 50 m), with a weather station co-located with the T0 station. Time series of Th and T0 were collected for 2020, when the Vulcano Fossa hydrothermal system was at a low and stable level of activity so that external drivers would have been the main influences on Th and T0, and hence also ΔT. To test for divergence from normality in terms of diurnal and seasonal variations in Th and T0, and the role of external factors in causing abnormality, we used the deep learning engine DITAN: a domain-agnostic framework to detect and interpret anomalies in time-series data.Results: During the year, DITAN found 16 cases of two types of meteorological events: intense low-pressure systems and high-intensity rainstorms (cloudbursts). Passage of 13 abnormal low-pressure systems were detected (10 between February and May, and three in December), with three abnormal rainstorm events (all in December); all three being coincident with the abnormal low pressure events. We find just two abnormalities in the time series for of Th and T0, both of which coincide with passage of abnormal low-pressure systems, and neither of which coincide with abnormal rain events. We conclude that diurnal and annual heating and cooling cycles, subject to normal meteorological inputs and at a surface above a geothermal-heated source, are immune to anomalous behaviour to the external (meteorological) variations.

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Section: Figure 13mentioning

confidence: 98%

Section: Background and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

External factors driving surface temperature changes above geothermal systems: answers from deep learning

Giannoulis,

Pailot-Bonnétat,

Barra

et al. 2024

Front. Earth Sci.

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Inversion of heat loss to obtain conductivity, density, and permeability at bottom-heated surfaces: the case of the hydrothermal system at Vulcano between 2019 and 2023

Harris,

Pailot-Bonnétat

2024

Bull Volcanol

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At hydrothermal systems, heat transfer across the nal surface layer is driven by permeable convection and conduction, so that permeability and conductivity play fundamental roles in controlling the heat ux to the atmosphere. We build a Rayleigh-number driven heat transfer model for a bottom-heated surface that uses measurements of heat ux density (radiation and convection to the atmosphere in W/m²), surface temperature, and soil temperature to solve for soil conductivity, density, and permeability. At Vulcano in 2019, we measured an ASTER-derived heat ux density of 240 ± 70 W/m², and a difference between soil and surface temperature of 18 ± 6°C. The surface layer is a 7.5 ± 2.5 cm thick case hardened crust across which heat transfer is conduction dominated. We invert our heat transfer model by using the derived temperature (T) gradient of T = -49.7y² + 113.6y + 35 (R² = 0.9997), where y is depth in meters between the surface and 70 cm. The result is a conductivity for the case hardened layer of 1.0 ± 0.3 W/(m K) and density of 2440 ± 120 kg/m 3 . Below the case harded layer heat transfer is dominated by permeable convection, and a soil comprised of highly altered trachytic blocks in an ash matrix. Our model gives permeabilities of 1-19 × 10 − 10 m² of this layer in 2019. In 2021, Vulcano entered a phase of unrest.Our model reveals that this was associated with an increase in permeability to 10 − 7 m². However, by 2023 permeabilities had reverted to pre-unrest levels. Using simple measurements of surface and soil temperature, coupled with heat ux density from a satellite overpass, the model can be used as a basis to constrain heat transfer and to assess permeability at any hydrothermal system.

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Inversion of heat loss to obtain conductivity, density and permeability at bottom-heated surfaces: The case of hydrothermal system at Vulcano between 2019 and 2023

Harris,

Pailot-Bonnétat

2024

Preprint

Self Cite

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At hydrothermal systems, heat transfer across the final surface layer is driven by permeable convection and conduction, so that permeability and conductivity play fundamental roles in controlling the heat flux to the atmosphere. We build a Rayleigh-number driven heat transfer model for a bottom-heated surface that uses measurements of heat flux density (radiation and convection to the atmosphere in W/m²), surface temperature, and soil temperature to solve for soil conductivity, density, and permeability. At Vulcano in 2019, we measured an ASTER-derived heat flux density of 240 ± 70 W/m², and a difference between soil and surface temperature of 18 ± 6°C. The surface layer is a 7.5 ± 2.5 cm thick case hardened crust across which heat transfer is conduction dominated. We invert our heat transfer model by using the derived temperature (T) gradient of T = -49.7y² + 113.6y + 35 (R² = 0.9997), where y is depth in meters between the surface and 70 cm. The result is a conductivity for the case hardened layer of 1.0 ± 0.3 W/(m K) and density of 2440 ± 120 kg/m3. Below the case harded layer heat transfer is dominated by permeable convection, and a soil comprised of highly altered trachytic blocks in an ash matrix. Our model gives permeabilities of 1–19 × 10− 10 m² of this layer in 2019. In 2021, Vulcano entered a phase of unrest. Our model reveals that this was associated with an increase in permeability to 10− 7 m². However, by 2023 permeabilities had reverted to pre-unrest levels. Using simple measurements of surface and soil temperature, coupled with heat flux density from a satellite overpass, the model can be used as a basis to constrain heat transfer and to assess permeability at any hydrothermal system.

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A Thermal Record for Unrest at Vulcano 2020–2022: In Situ Meteorological Data and Soil Temperature Recorded at High Temporal Resolution

Cited by 3 publications

References 14 publications

External factors driving surface temperature changes above geothermal systems: answers from deep learning

External factors driving surface temperature changes above geothermal systems: answers from deep learning

Inversion of heat loss to obtain conductivity, density, and permeability at bottom-heated surfaces: the case of the hydrothermal system at Vulcano between 2019 and 2023

Inversion of heat loss to obtain conductivity, density and permeability at bottom-heated surfaces: The case of hydrothermal system at Vulcano between 2019 and 2023

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