Leveraging High-Performance Computing for Enhanced Lava Flow Forecasting Workflow

Characterization, origin, and evolution of one of the most eroded mafic monogenetic fields within the central Andes: The case of El País lava flow field, northern Chile

The thesis contributed to the volcanic hazard assessment through the reconstruction of some historical flank eruptions of Etna in order to obtain quantitative data (volumes, effusion rates, etc.) for characterizing the recent effusive activity, quantifying the impact on the territory and defining mitigation actions for reducing the volcanic risk as for example containment barriers. The reconstruction was based on a quantitative approach using data extracted from aerial photographs and topographic maps. The approach allows to obtain the temporal evolution of the lava flow field and estimating the Time Average Discharge Rate (TADR) by dividing the volume emplaced over a given time interval for the corresponding duration. The analysis concerned the 2001, 1981 and 1928 Etna eruptions. The choice of these events is linked to their impact on inhabited areas. The results of the analysis showed an extraordinarily high effusion rate for the 1981 and 1928 eruptions (over 600 m^3/s), unusual for Etna eruptions. For the 1981 Etna eruption an eruptive model was proposed to explain the high discharge rate. The obtained TADRs were used as input data for simulations of the propagation of the lava flows for evaluating different scenarios of volcanic hazard and analyse different mitigation actions against lava flow invasion. It was experienced how numerical simulations could be adopted for evaluating the effectiveness of barrier construction and for supporting their optimal design. In particular, the gabions were proposed as an improvement for the construction of barriers with respect to the earthen barriers. The gabion barriers allow to create easily modular structures reducing the handled volumes and the intervention time. For evaluating operational constrain an experimental test was carried out to test the filling of the gabions with volcanic rock and evaluating their deformation during transport and placement.

When a lava flow enters a body of water, either a lake, sea, river or ocean, explosive interaction may arise. However, when it is an 'a'ā lava flow entering water, a more complex interaction occurs, that is very poorly described and documented in literature. In this paper, we analysed the 2–4 ka San Bartolo lava flow field emplaced on the north flank of Stromboli volcano, Italy. The lava flow field extends from ~ 650 m a.s.l. where the eruptive fissure is located, with two lava channels being apparent on the steep down to the coast. Along the coast the lava flow field expands to form a lava delta ~ 1 km wide characterised by 16 lava ‘Flow’ units. We performed a field survey to characterise the features of lava entering the sea and the associated formation of different components and magnetic measurements to infer the flow fabrics and emplacement process of the lava flow system. We measured the density, porosity and connectivity of several specimens to analyse the effect of lava-water interaction on the content in vesicles and their connectivity and conducted a macroscopic componentry analysis (clast count) at selected sites to infer the character of the eroded offshore segment of the lava flow field and its component flow units. The collected data allowed us to define the main components of a lava delta fed by 'a'ā lava flows, with its channels, littoral units, ramps, lava tubes, and inflated pāhoehoe flows controlled by the arterial 'a'ā flow fronts. The spatial organisation of these components allowed us to build a three-step descriptive model for 'a'ā entering a water. The initial stage corresponds to the entry of channel-fed 'a'ā lava flow into the sea which fragments to form metric blocks of 'a'ā lava. Continued lava supply to the foreshore causes flow units to stall while spreading over this substrate. Subsequent 'a'ā lava flow units ramp up behind the stalled flow front barrier. Lava tubes extending through the stalled flow barrier feed the seaward extension of a bench made of several pāhoehoe flow units.

Lava tubes from the Paraná-Etendeka Continental Flood Basalt Province: Morphology and importance to emplacement models

La colada de Cervantes se ubica en el flanco sureste del volcan Irazu, en Costa Rica, y es el flujo de lava mas reciente y grande datado en este volcan. Este campo esta constituido por dos flujos de lava que presentan una compo- sicion diferente. El flujo occidental (10,85 km2, 0.18 ± 0.1 km3) es de composicion basaltica (SiO2: 50,71-52,06%, MgO: 8,13-9,50%) y basaltica andesitica debido a mezcla de magma (SiO2: 52,90-53,42%, MgO: 6,71-7,12%), y es mas viejo (57 ka ± 13 ka anos a.P.). El flujo oriental (28,14 km2, 1,14 ± 0,25 km3) es de composicion basaltica andesita (SiO2: 55,54- 56.58%, MgO: 4,4-5,26%) y es mas joven (~16 840 anos a.P.). La colada de Cervantes muestra distintas morfologias superficiales tales como canales, levees y topografia tipo hummocky. La estructura interna de la colada esta compuesta por varias unidades de lava masiva e intercalaciones de auto brechas (con presencia de bolas de lava). El flujo Occidental esta compuesto por flujos de lava de tipo escoriacea y flujos en bloques, los cuales a su vez estan cubiertos de ceniza, y con un espesor inferior a 20 m, con excepcion del rio Reventazon, en donde posee un espesor de aproximadamente 150 m, creando a su vez el paleo-lago Cachi. Sin embargo, el flujo Occidental presenta una composicion mas basica (posiblemen- te mayor fluidez), pequenas dimensiones, tanto en espesor como extension, resultado de bajas tasas de efusion, pendientes altas, y/o el cese de efusion de lava. El flujo Oriental fue originado a partir de fisuras ubicadas en sitios de baja altitud, y su espesor y extension, ademas de la presencia de levees, son caracteristicas que permiten relacionarlo con periodos de mayor efusion o descarga. Unicamente el flujo Oriental tiene zonas de no depositacion de lava (kipukas) y una serie de depresiones de diferentes dimensiones y un patron sinuoso, resultado de un antiguo valle fluvial cubierto por la lava. En el flanco sur del Irazu presenta un par tectonico conjugado (N34oE and N26oW, σ1 orientado N04°E) conformado por una serie de fisuras, crateres y conos, con una longitud de 2 km, estructuras que constituyen la fuente de emision de la colada de Cervantes. Dichos sectores podrian eventualmente presentar en el futuro actividad eruptiva.

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.

Ensuring a reliable water supply in the face of changing conditions and growing demand is a critical global challenge. Water distribution networks (WDNs) are essential infrastructure, but traditional modeling methods based on historical data often struggle to adapt in real-time and integrate new information. In order to lower model errors in WDN models, this study explores the use of a Data Assimilation (DA) method that makes use of the Ensemble Kalman Filter. The study explores the effectiveness of the DA method, and a novel Greedy Algorithm (GA) for optimizing the location of sensors. The study shows that the DA method improves the WDN model and the GA is able to successfully determine optimal sensor locations. It was observed that increasing the number of sensors in the WDN increased the effectiveness of the DA method. This study highlights the potential of data assimilation to improve WDN modeling. Water utilities can gain from more precise predictions, greater system performance, and efficient decision-making for the management and maintenance of water distribution networks by allowing models to adapt and improve dynamically

Quantifying continental-scale river discharge is essential to understanding the terrestrial water cycle but is susceptible to errors caused by a lack of observations and the limitations of hydrodynamic modeling. Data assimilation (DA) methods are increasingly used to estimate river discharge in combination with emerging river-related remote sensing products (e.g., water surface elevation [WSE], water surface slope, river width, and flood extent). However, directly comparing simulated WSE to satellite altimetry data remains challenging (e.g., because of large biases between simulations and observations or uncertainties in parameters), and large errors can be introduced when satellite observations are assimilated into hydrodynamic models. In this study we performed direct, anomaly, and normalized value assimilation experiments to investigate the capacity of DA to improve river discharge within the current limitations of hydrodynamic modeling. We performed hydrological DA using a physically-based empirical localization method applied to the Amazon Basin. We used satellite altimetry data from ENVISAT, Jason 1, and Jason 2. Direct DA was the baseline assimilation method and was subject to errors due to biases in the simulated WSE. To overcome these errors, we used anomaly DA as an alternative to direct DA. We found that the modeled and observed WSE distributions differed considerably (e.g., differences in amplitude, seasonal flow variation, and a skewed distribution due to limitations of the hydrodynamic models). Therefore, normalized value DA was performed to improve discharge estimation. River discharge estimates were improved at 24 %, 38 %, and 62 % of stream gauges in the direct, anomaly, and normalized value assimilations relative to simulations without DA. Normalized value assimilation performed best for estimating river discharge given the current limitations of hydrodynamic models. Most gauges within the river reaches covered by satellite observations accurately estimated river discharge, with Nash-Sutcliffe efficiency (NSE) > 0.6. The amplitudes of WSE variation were improved in the normalized DA experiment. Furthermore, in the Amazon Basin, normalized assimilation (median NSE = 0.50) improved river discharge estimation compared to open-loop simulation with the global hydrodynamic model (median NSE = 0.42). River discharge estimation using direct DA methods was improved by 7 % with calibration of river bathymetry based on NSE. The direct DA approach outperformed the other DA approaches when runoff was considerably biased, but anomaly DA performed best when the river bathymetry was erroneous. The uncertainties in hydrodynamic modeling (e.g., river bottom elevation, river width, simplified floodplain dynamics, and the rectangular cross-section assumption) should be improved to fully realize the advantages of river discharge DA through the assimilation of satellite altimetry. This study contributes to the development of a global river discharge reanalysis product that is consistent spatially and temporally.

We present studies on the physical volcanology of the ∼15 Ma Roza Member of the Wanapum Formation in the Columbia River Basalt Group. The Roza Member represents a compound pahoehoe flood basalt lava flow field, with an area of ∼40,300 km2 and a volume of ∼1300 km3. It consists of 4 major lava flows each composed of numerous, decimeter to kilometer long pahoehoe lobes. Roza lavas feature a wide range of pahoehoe surface structures, such as lava rise plateaus, tumuli, and surface breakouts, and we illustrate that the lava morphology is inconsistent with previous proposals of rapid emplacement for these lavas. An integral component of the Roza flow field is the sheet lobe with internal structures identical to those of inflated pahoehoe sheet lobes from Hawaii and Iceland, both at the same scale and at much larger scales. We identify a three‐part division of the sheet lobes into basal crust, lava core, and lava crust, which are interpreted as the equivalent to the bottom crust, the liquid lava core, and the surface crust of an active inflating pahoehoe lobe. The upper lava crust grows continuously during lava emplacement and its growth rate can be approximated by conductive cooling. This relationship is used to calculate the emplacement time for individual Roza sheet lobes and to derive a first‐order estimate on the duration of the Roza eruption. The results indicate that the emplacement of individual Roza lobes lasted for months to years and that the lava flow field was constructed over a period of at least 14 years. We propose that the Roza flows achieved great areal dimensions and thicknesses by inflation and endogenous growth. As the lava flowed from vent to flow front it traveled under an insulating crust which maintained cooling rates of <0.1 °C/km and allowed for efficient transport of lava over distances up to 300 km.

Challenges for data assimilation – from convective‐scale to climate

SummaryEstimating river discharge from in situ and/or remote sensing data is a key issue for evaluation of water balance at local and global scales and for water management. Variational data assimilation (DA) is a powerful approach used in operational weather and ocean forecasting, which can also be used in this context. A distinctive feature of the river discharge estimation problem is the likely presence of significant uncertainty in principal parameters of a hydraulic model, such as bathymetry and friction, which have to be included into the control vector alongside the discharge. However, the conventional variational DA method being used for solving such extended problems often fails. This happens because the control vector iterates (i.e., approximations arising in the course of minimization) result into hydraulic states not supported by the model. In this paper, we suggest a novel version of the variational DA method specially designed for solving estimation‐under‐uncertainty problems, which is based on the ideas of iterative regularization.The method is implemented with SIC2, which is a full Saint‐Venant based 1D‐network model. The SIC2 software is widely used by research, consultant and industrial communities for modeling river, irrigation canal, and drainage network behavior. The adjoint model required for variational DA is obtained by means of automatic differentiation. This is likely to be the first stable consistent adjoint of the 1D‐network model of a commercial status in existence.The DA problems considered in this paper are offtake/tributary estimation under uncertainty in the cross‐device parameters and inflow discharge estimation under uncertainty in the bathymetry defining parameters and the friction coefficient. Numerical tests have been designed to understand identifiability of discharge given uncertainty in bathymetry and friction. The developed methodology, and software seems useful in the context of the future Surface Water and Ocean Topography satellite mission. Copyright © 2016 John Wiley & Sons, Ltd.

Surface observations can provide crucial information for NWP models. If not assimilated carefully, however, they can degrade forecast accuracy, especially in complex terrains like the Alps. The horizontal and vertical covariances of climatological background error covariances used in the three-dimensional variational (3DVar) data assimilation (DA) method can produce unrealistic increments over sloped terrain. For instance, an observation from a valley station can still generate increments at the mountaintop, even though the valley observation may not accurately represent the mountaintop's weather conditions. We used a hybrid three-dimensional ensemble variational (Hybrid-3DEnVar) DA method to address this issue, incorporating a 50-member convection-permitting ensemble. This method was recently tested in Geosphere Austria's convective scale limited-area NWP model AROME at a 2.5 km horizontal resolution. We assimilated 2-meter temperature, 2-meter relative humidity, geopotential, and 10-meter wind components from 680 surface stations, including the Austrian TAWES network and SYNOP observations from neighbouring countries. 400 stations were actively assimilated from the observation dataset, and the rest were used to verify the analysis.  Our results present the effectiveness of this newly tested Hybrid-3DEnVar against GeoSphere Austria's operational 3DVar in assimilating surface observations over complex Alpine terrain.

Reconstructing spatiotemporal systems from sparse observations remains a long-standing challenge in several domains, including geoscience, air pollution and fluid dynamics. While various data assimilation (DA) and machine learning (ML) methods have shown potential, they still face significant challenges (see [1]): The computational burden of conventional DA algorithms (including error covariance specification), particularly for multivariate, high-dimensional systems. Sparse and movable sensor placement, which makes conventional ML models (typically requiring fixed and regularly distributed input data) cumbersome. The ill-defined nature of the sparse reconstruction problem, which poses significant risks of overfitting. We will present our recent works aimed at addressing these challenges. More specifically, we developed latent DA algorithms [2] to reduce the computational burden of variational DA methods. These algorithms demonstrate great potential in efficiently assimilating sparse observations within a reduced-order latent space constructed by neural networks, thanks to the TorchDA library [3]. The latter enables GPU implementation of mainstream data assimilation methods and supports non-explicit state-observation transformation functions, provided they can be learned by a neural network.We have also employed advanced deep learning techniques, including Voronoi-tessellation CNNs [4] and Vision Transformer-based autoencoders [5], to learn mappings from sparse observations to the complete physical space. These approaches effectively address challenges such as movable sensor placements and varying sensor numbers. Their integration with DA algorithms has also been evaluated.Finally, our recent work explores [6] the utility of generative AI techniques, particularly denoising diffusion models, for field reconstruction from sparse observations. Generative AI methods offer two main advantages: first, they produce a sample from a probability distribution rather than predicting the mean as a fixed output, which can help mitigate overfitting caused by the illy-defined problem. Second, they inherently function as ensemble predictors by generating several samples, facilitating uncertainty quantification, which is essential in data assimilation. The numerical results tested on cases ranging from fluid dynamics benchmarks to semi-operational air pollution simulations will also be discussed.[1] Cheng, S., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., Xiao, D., Janjic, T., Ding, W., Guo, Y., Carrassi, A., Bocquet, M. and Arcucci, R, 2023. Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review. IEEE/CAA Journal of Automatica Sinica[2] Cheng, S., Chen, J., Anastasiou, C., Angeli, P., Matar, O.K., Guo, Y.K., Pain, C.C. and Arcucci, R., 2023. Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models. Journal of Scientific Computing[3] Cheng, S., Min, J., Liu, C. and Arcucci, R., 2025. TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions. Computer Physics Communications[4] Cheng, S., Liu, C., Guo, Y. and Arcucci, R., 2024. Efficient deep data assimilation with sparse observations and time-varying sensors. Journal of Computational Physics[5] Fan, H., Cheng, S., de Nazelle, A.J. and Arcucci, R., 2024. ViTAE-SL: a vision transformer-based autoencoder and spatial interpolation learner for field reconstruction. Computer Physics Communications [6] Zhuang, Y., Cheng, S. and Duraisamy, K., 2025. Spatially-aware diffusion models with cross-attention for global field reconstruction with sparse observations. Computer Methods in Applied Mechanics and Engineering

Mapping and DOWNFLOW simulation of recent lava flow fields at Mount Etna

Subsurface architecture of Las Bombas volcano circular structure (Southern Mendoza, Argentina) from geophysical studies