Quantifying lava flow hazards in response to effusive eruption

Abstract. Marine stratocumulus (Sc) clouds play an essential role in the earth radiation budget. Here, we compare liquid water path (LWP), cloud optical thickness (τ), and cloud droplet effective radius (re) retrievals from 2 years of collocated Spinning Enhanced Visible and Infrared Imager (SEVIRI), Moderate Resolution Imaging Spectroradiometer (MODIS), and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations; estimate the effect of biomass burning smoke on passive imager retrievals; and evaluate the diurnal cycle of South Atlantic marine Sc clouds.The effect of absorbing aerosols from biomass burning on the retrievals was investigated using the aerosol index (AI) obtained from the Ozone Monitoring Instrument (OMI). SEVIRI and MODIS LWPs were found to decrease with increasing AI relative to TMI LWP, consistent with well-known negative visible/near-infrared (VIS/NIR) retrieval biases in τ and re. In the aerosol-affected months of July–August–September, SEVIRI LWP – based on the 1.6 µm re – was biased low by 14 g m−2 ( ∼ 16 %) compared to TMI in overcast scenes, while MODIS LWP showed a smaller low bias of 4 g m−2 ( ∼ 5 %) for the 1.6 µm channel and a high bias of 8 g m−2 ( ∼ 10 %) for the 3.7 µm channel compared to TMI. Neglecting aerosol-affected pixels reduced the mean SEVIRI–TMI LWP bias considerably. For 2 years of data, SEVIRI LWP had a correlation with TMI and MODIS LWP of about 0.86 and 0.94, respectively, and biases of only 4–8 g m−2 (5 %–10 %) for overcast cases.The SEVIRI LWP diurnal cycle was in good overall agreement with TMI except in the aerosol-affected months. Both TMI and SEVIRI LWP decreased from morning to late afternoon, after which a slow increase was observed. Terra and Aqua MODIS mean LWPs also suggested a similar diurnal variation. The relative amplitude of the 2-year-mean and seasonal-mean LWP diurnal cycle varied between 35 % and 40 % from morning to late afternoon for overcast cases. The diurnal variation in SEVIRI LWP was mainly due to changes in τ, while re showed only little diurnal variability.

The quality of Earth observation (EO) based vegetation monitoring has improved during recent years, which can be attributed to the enhanced sensor design of new satellites such as MODIS (Moderate Resolution Imaging Spectroradiometer) on Terra and Aqua. It is however expected that sun-sensor geometry variations will have a more visible impact on the Normalized Difference Vegetation Index (NDVI) from MODIS compared to earlier data sources, since noise related to atmosphere and sensor calibration is substantially reduced in the MODIS data stream. For this reason, the effect of varying MODIS viewing geometry on red, near-infrared (NIR) and NDVI needs to be quantified. Data from the geostationary MSG (Meteosat Second Generation) SEVIRI (Spinning Enhanced Visible and Infrared Imager) sensor is well suited for this purpose due to the fixed position of the sensor, the spectral resolution, including a red and NIR band, and the high temporal resolution (15 min) of data, enabling MSG data to be used as a reference for estimating MODIS surface reflectance and NDVI variations caused by varying sun-sensor geometry. The study was performed on data covering West Africa for periods of lowest possible cloud cover for three consecutive years (2004–2006). An analysis covering the entire range of NDVI revealed day-to-day variations in observed MODIS NDVI of 50–60% for medium dense vegetation (NDVI ≈ 0.5) caused by variations in MODIS view zenith angles (VZAs) between nadir and the high forward-scatter view direction. Statistical analysis on red, NIR and NDVI from MODIS and MSG SEVIRI for three transects (characterized by different vegetation densities) showed that both MODIS red and NIR reflectances are highly dependant on MODIS VZA and relative azimuth angle (RAA), due to the anisotropic behaviour of red and NIR reflectances. The anisotropic reflectance in the red and NIR band was to some degree minimized by the ratioing properties of NDVI. The minimization by the NDVI normalization is very dependent on the vegetation density however, since the degree of anisotropy in red and NIR reflectances depends on the amount of vegetation present. MODIS VZA and RAA effects on NDVI were highest for medium dense vegetation (NDVI ≈ 0.5–0.6). The VZA and RAA effects were less for sparsely vegetated areas (NDVI ≈ 0.3–0.35) and the smallest effect on NDVI was found for dense vegetation (NDVI ≈ 0.7). These results have implications for the end users' interpretation of NDVI, and challenge the expediency of the MODIS NDVI compositing technique, which should be refined to distinguish between forward- and backward-scatter viewing direction by taking RAA into account.

We have used a suite of remotely sensed data, numerical lava flow modeling, and field observations to determine quantitative characteristics of the 1995 Fernandina and 1998 Cerro Azul eruptions in the western Galapagos Islands. Flank lava flow areas, volumes, instantaneous effusion rates, and average effusion rates were all determined for these two eruptions, for which only limited syn-eruptive field observations are available. Using data from SPOT, TOPSAR, ERS-1, and ERS-2, we determined that the 1995 Fernandina flow covers a subaerial area of 6.5×106 m2 and has a subaerial dense rock equivalent (DRE) volume of 42×106 m3. Field observations, ATSR satellite data, and the FLOWGO numerical model allow us to determine that the effusion rate declined exponentially from a high of ~60–200 m3 s-1 during the first few hours to <5 m3 s-1 prior to ceasing after 73 days, with a mean effusion rate of 4–16 m3 s-1. Integrating the ATSR-derived, exponentially declining effusion rate over the eruption duration produces a total (subaerial + submarine) DRE volume of between 27 and 100×106 m3, the range in values being due to differing assumptions about heat loss characteristics; only values in the higher part of this range are consistent with the independently derived subaerial volume. Using SPOT, TOPSAR, ERS-1, and ERS-2 data, we determine that the 1998 Cerro Azul flow is 16 km long, covers 16 km2, and has a DRE volume of 54×106 m3. FLOWGO produces at-vent velocity and effusion rate values of 11 m s-1 and ~600 m3 s-1, respectively. The velocity value agrees well with the 12 m s-1 estimated in the field. The mean effusion rate (total DRE volume/duration) was 7–47 m3 s-1. Dike dimensions, fissure lengths, and pressure gradients along the conduit based on magma chamber depth estimates of 3–5 km produce mean effusion rates for the two eruptions that range over nearly four orders of magnitude, the range being due to uncertainty in the magma viscosity, dike dimensions, and pressure gradient between magma chamber and vent. Although somewhat consistent with mean effusion rates from other techniques, their wide range makes them less useful. The exponentially declining effusion rates during both eruptions are consistent with release of elastic strain being the driving mechanism of the eruptions. Our results provide independent input parameters for previously published theoretical relationships between magma chamber pressurization and eruption rates that constrain chamber volumes and increases in volume prior to eruption, as well as time constants of exponential decay during the eruption. The results and theoretical relationships combine to indicate that at both volcanoes probably 25–30% of the volumetric increase in the magma chamber erupted as lava onto the surface. In both eruptions the lava flow volumes are less than 1% of the magma chamber volume.

During the past decade an average of 60 volcanoes erupted each year, around 20 of which were erupting on any given day. Some of these, such as Erta Ale in Ethiopia, are persistently active, whereas others, such as Bezymianny in Russia, erupt more sporadically. Satellite remote sensing offers a convenient way to monitor changes in the thermal budgets of these volcanoes from space. Using data acquired by the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, we show how the amount of heat radiated into the Earth's atmosphere by 65 active volcanoes has varied between 26 February 2000 and 31 December 2006. We find that the radiant volcanic heat flux into the atmosphere occurs at a relatively steady‐state baseline level, superimposed on which are large thermal emission ‘spikes’. These anomalous emission events are associated with the emplacement of basaltic ‘a’a lava flows at volcanoes such as Mount Etna, Italy, and Nyamuragira, in the Democratic Republic of Congo. We also demonstrate how these data can be used to estimate lava effusion rates during effusive eruptions, and magma flux rates at persistently active volcanoes that host active lava lakes.

Africa is the single largest continental source of biomass burning emissions and one where emission source strengths are characterized by strong diurnal and seasonal cycles. This paper describes the development of a fire detection and characterization algorithm for generating high temporal resolution African pyrogenic emission data sets using data from the geostationary spinning enhanced visible and infrared imager (SEVIRI). The algorithm builds on a prototype approach tested previously with preoperational SEVIRI data and utilizes both spatial and spectral detection methods whose thresholds adapt contextually within and between imaging slots. Algorithm validation is carried out via comparison to data from ~800 temporally coincident moderate resolution imaging spectroradiometer (MODIS) scenes, and performance is significantly improved over the prior algorithm version, particularly in terms of detecting low fire radiative power (FRP) signals. On a per-fire basis, SEVIRI shows a good agreement with MODIS in terms of FRP measurement, with a small (3.7 MW) bias. In comparison to regional-scale total FRP derived from MODIS, SEVIRI underestimates this by, on average, 40% to 50% due to the nondetection of many low-intensity fire pixels (FRP &amp;lt; 50 MW). Frequency-magnitude analysis can be used to adjust fire radiative energy estimates for this effect, and taking this and other adjustments into account, SEVIRI-derived fuel consumption estimates for southern Africa from July to October 2004 are 259-339 Tg, with emission intensity peaking after midday and reducing by more than an order of magnitude each night.

Chapter 55 - Lava Flow Hazards and Modeling

Earth Observations (EO) play a crucial role in global monitoring of volcanic activity worldwide. Satellite data are continuously acquired by multispectralsensors on board of polar and geostationary satellites orbiting the Earth. EO allows to track ongoing volcanic activity by retrieving eruptive parameters such as the amount of lava erupted in near real time. The heterogeneity of satellite sensors in terms of spatial and temporal resolution requires advanced techniques to automatically estimate volcanic features and combine their outcomes. Here, the potential of integrating Artificial Intelligence (AI) techniques has been demonstrated in processing large amounts of heterogeneous satellite data used to monitor the effusive activity in near real time at Mount Etna from November 2022 to February 2023. Data provided by satellite sensors, including the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI), enable reliable estimates of Volcanic Radiative Power (VRP), areal coverage of lava flows, Time Averaged Discharge Rate (TADR) and the lava volume erupted, and areal coverage of lava flows thanks to Sentinel-2 MSI.

Abstract. The 6-month-long effusive eruption at the Fagradalsfjall volcano in 2021 is the most visited eruption site in Iceland to date (June 2023), and it needed intense lava flow hazard assessment. In this study we document how strategies for lava flow modeling were implemented using the stochastic model MrLavaLoba to evaluate hazards during this effusive event. Overall, the purposes were threefold: (a) pre-eruption simulations to investigate potential lava inundation of critical infrastructure, (b) syn-eruption simulations for short-term (2-week time frame) lava flow hazard assessment and (c) syn-eruption simulations for long-term (months to years) hazard assessments. Additionally, strategies for lava barrier testing were developed, and syn-eruption topographic models were incorporated into simulations in near real time. The model provided promising results that were shared regularly at stakeholder meetings with the monitoring personnel, scientists and civil-protection representatives helping to identify potential short-term and long-term lava hazards. This included evaluation of the timing of barrier overflow and the filling and spilling of lava from one valley to another. During the crisis the MrLavaLoba model was updated to increase functionality such as by considering multiple active vents. Following the eruption, the model was optimized substantially, decreasing the computational time required for the simulations and speeding up the delivery of final products.

Multispectral data acquired by Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard the geostationary platform Meteosat Second Generation (MSG2), located at 0deg longitude, were analyzed to assess the payload suitability for quantitative volcano monitoring. In times of good infrared transparency of the troposphere, SEVIRI data proved particularly suited to detect the onset of effusive eruptions and to monitor the related lava flows with 15-min refresh rates. After development of a code allowing for fast automated processing of the 96 data streams downlinked daily by MSG2, thorough monitoring tests were carried out on the 2007 eruptions of Stromboli (Aeolian Islands, Italy) and Piton de la Fournaise (Reunion Island, France). The results in radiant flux and effusion rate provided by use of thermal, mid-, and short infrared bands of SEVIRI were validated by exploiting simultaneous MODIS overpasses by Terra and Aqua, up to four times daily.

The intercomparison of LWP retrievals from observations by a geostationary satellite imager [Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG)] and a ground-based microwave (MW) radiometer is examined in the context of the inhomogeneity of overcast cloudy skies. Although the influence of cloud inhomogeneity on satellite observations has received much attention, relatively little is known about its impact on validation studies. Given SEVIRI’s large field of view (3 km × 6 km for northern Europe), especially when compared to the narrow width of the radiometer tracks (100–200 m), cloud inhomogeneity may be expected to significantly affect the satellite retrieval validation. This paper quantifies the various validation uncertainties resulting from cloud inhomogeneities and proposes an approach to minimize these uncertainties. The study is performed by simulating both satellite and ground-based observations through resampling a set of high-resolution (100 m) cloud fields that are derived from 1 km × 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The authors’ technique for generating realistic high-resolution LWP fields preserves the information present in the original observations and creates extra LWP variation at smaller-length scales by considering clouds as simple fractals. The authors believe that this is a new technique for creating high-resolution LWP fields. Validation errors resulting from cloud inhomogeneity can be classified in two groups. The first group relates entirely to the retrieval process for satellite observations and includes the well-known plane-parallel bias as well as field-of-view mismatches between different channels used in the retrieval. The second group relates to differences in the scene observed by satellite and ground-based sensors. This includes systematic shifts in the observed scene resulting from viewing conditions (parallax effect), offsets between satellite images and ground sites, and different fields of view. Results indicate that the plane-parallel bias for the authors’ sample of 604 clouds has a median value of −3.3 g m−2. All other error contributions appear to be random and have no biases. For individual observations, the parallax effect easily dominates the total error budget for sites that are observed under large viewing angles (e.g., northern Europe). The authors show that this error may be partly compensated by using information about cloud-top heights and by spatially interpolating among an array of SEVIRI pixels to obtain the best estimate of the satellite-retrieved LWP value over the ground site. Optimal intercomparison of satellite and ground-based observations is also possible by matching the track length of the ground observations to the imager’s pixel size in the wind direction. Thus, one surprising conclusion is that the LWP errors resulting from the second group (scene differences) are significantly larger than those resulting from the first group (satellite retrieval), even after corrections have been applied. Smaller satellite pixels do not alleviate the problem but rather aggravate it, unless the parallax error is corrected. Temporal or spatial averages of observations may be used to reduce the random errors, but the statistical properties of such aggregates are, at the moment, not obvious for reasons that will be discussed. Calibration errors are not considered in the present study.

Comparison of the response of the Landsat 7 Enhanced Thematic Mapper Plus and the Earth Observing-1 Advanced Land Imager over active lava flows

The dynamics and morphology of lava flows are influenced by lava viscosity. However, the rheological parameters used in lava flow modelling are often subject to considerable uncertainties. To address this, we analyse the influence of key rheological parameters on the advancement and morphology of lava flows using GPUFLOW, a two-dimensional cellular automaton model that simulates Bingham fluid flow on the flanks of Mt. Etna. Our results demonstrate that variations in the initial volume fraction of crystals, the volume fraction of crystals in the equilibrium state, the characteristic time of crystal content growth (i.e. the relaxation time), the melt viscosity, and yield strength significantly impact lava flow patterns. An increased volume fraction of crystals – either at the time of eruption or in the equilibrium state – is associated with reduced advancement of the lava flow. This reduction is attributed to increased viscosity, which promotes vertical thickening of the lava flow and limits its lateral extent. In contrast, when relaxation times exceed the lava flow runout times, the crystal content evolves slowly, allowing the lava flow to advance further with a viscosity close to that of the crystal-free melt viscosity. Lower melt viscosity enhances lava flow mobility, whereas higher melt viscosity encourages shorter and thicker flows. Additionally, the slope of the volcano flank, yield strength, and melt viscosity influence the lava critical thickness, thereby exerting control over the extent of lava advance. These findings provide improved insights into the rheological controls on lava flow dynamics and have important implications for flow hazard assessment and forecasting of lava flows.Graphical

ABSTRACTA very simple algorithm with which to assess cloudiness over the Baltic Sea has been developed. The algorithm involves analysis of cloud longwave emissivity, supplemented with available information on shortwave emissions. The simple classification method is based on the split-window technique and compares a signal recorded in the neighbouring spectral bands with results of cloud-free atmosphere modelling. The algorithm was tested using data from the SEVIRI (Spinning Enhanced Visible and InfraRed Imager) satellite radiometer, but the method can be easily implemented for other data sources. Compatibility with instant shipborne radiation measurements was obtained. The effectiveness of the comparison was determined from the Hanssen–Kuiper score. The CM SAF (Satellite Application Facility on Climate Monitoring) data comparison shows that the system correctly classified areas in 88%, the satellite map accuracy was 46% with a 54% overvaluation error. The accuracy of the detection system was 44%, and the underestimation error was 56%. The comparison of modelling results showed 71% of correctly classified areas, the accuracy of the satellite map was 35%, and the repricing error was 65%. The accuracy of the detection system was 36%, and the underestimation error was 64%. The concordance of satellite measurements (Cohen’s kappa coefficient) was at a level of 98% and 99% between the satellite and model. Statistically, the analysis is based on the results of measurements for the Baltic Sea basin in 2015. The obtained data on the monthly cloudiness was compared with the operational, model and lidar data. A difference between the solution proposed and the approaches derived from prognostic models is demonstrated. The regional scale solution is shown to be a better representation of the true state of the atmosphere, compared to global-scale models. The results obtained show cloudiness assessments to differ depending on the model applied (CM SAF, UMPL – Unifed Model for Poland). The average annual cloudiness of the Baltic Sea is around 58.5%. It is a repeatable value by other solutions based on various methods and sources (MODIS – Moderate Resolution Imaging Spectroradiometer), including lidar ones (CALIPSO – Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation). In addition, a potential for applying the regional cloudiness assessment to estimate the top-down components of the radiation budget is illustrated.

Accurate mapping of recent lava flows can provide significant insight into the development of flow fields that may aid in predicting future flow behavior. The task is challenging, due to both intrinsic properties of the phenomenon (e.g., lava flow resurfacing processes) and technical issues (e.g., the difficulty to survey a spatially extended lava flow with either aerial or ground instruments while avoiding hazardous locations). The huge amount of moderate to high resolution multispectral satellite data currently provides new opportunities for monitoring of extreme thermal events, such as eruptive phenomena. While retrieving boundaries of an active lava flow is relatively straightforward, problems arise when discriminating a recently cooled lava flow from older lava flow fields. Here, we present a new supervised classifier based on machine learning techniques to discriminate recent lava imaged in the MultiSpectral Imager (MSI) onboard Sentinel-2 satellite. Automated classification evaluates each pixel in a scene and then groups the pixels with similar values (e.g., digital number, reflectance, radiance) into a specified number of classes. Bands at the spatial resolution of 10 m (bands 2, 3, 4, 8) are used as input to the classifier. The training phase is performed on a small number of pixels manually labeled as covered by fresh lava, while the testing characterizes the entire lava flow field. Compared with ground-based measurements and actual lava flows of Mount Etna emplaced in 2017 and 2018, our automatic procedure provides excellent results in terms of accuracy, precision, and sensitivity.

Lava flow emplacement depends on the effusion rate, the viscosity of the lava and the topography. The transport of lava within lava flows usually occurs through the formation of lava channels or by the development of lava tubes. Lava flows that form tubes are able to feed their front with molten lava, due to the insulation of the erupted lava by the roof of the tubes, preventing atmospheric cooling. These tube-fed lava flows can thus emplace over longer distances, increasing the hazard caused by lava flows. Lava tube formation within a lava flow can be influenced by different factors such as: eruption rate, pre-eruptive topography, geochemical composition, and lava viscosity. Understanding how these mechanisms influence the formation of a lava tube is of great importance to better assess the hazard caused by lava flows and better react in case of eruptive crisis. In this study, the 1858 lava flow field formed during the 1858-1861 eruption at Vesuvius was examined as it formed several lava tubes, including one which can be accessed. The 1858 lava flow field and the largest lava tubes were sampled from the vent to the front and from the inside to the outside respectively. We performed Electron Microprobe Analysis (EMPA) to measure crystal and glass compositions, and used phase-contrast synchrotron X-ray computed microtomography (&amp;#181;-CT) to retrieve the 3D structure of our samples and characterize the crystal phases. Our results show a variation between the microlitic matrix of the samples in terms of size and number of crystals, suggesting different cooling conditions during lava flow emplacement and lava tube development. Moreover, the volume fractions of the different phases composing the lava, leucites and plagioclase, clinopyroxenes, oxides, also show variations. These variations in volume fraction, number and size of the microlitic matrix can be used to decipher the local conditions of emplacement within the lava flow and lava tube. Understanding the petrology and geochemistry of the lavas forming the flow field and the lava tube help us to further comprehend lava flow emplacement and lava tube formation at Vesuvius.