Controls on Lava Flow Emplacement on Low-Gradient Terrain: Insights from the SP Lava Flow (San Francisco Volcanic Field, USA)

The paths of lava flows are well predictable following the gradient of the terrain, however, at near flat topography flow reconstruction is challenging. This problem stems from the importance of small barriers and topographic complexities affecting low-gradient terrain lava flows.  Lava flows emplaced on low-gradient surfaces can propagate in a wide range of directions, making hazard forecasting highly sensitive to initial conditions and topographic representation. These challenges are compounded by long repose times between eruptions, which often result in poorly constrained pre-eruptive surfaces that must be reconstructed to achieve meaningful comparisons between simulated and observed lava flows.

The San Francisco Volcanic Field (SFVF), one of the largest in the continental United States, poses a significant lava-flow hazard due to eruptions such as Sunset Crater (~1085 CE) and the SP lava flow (5.5–6 ka). Assessing lava flow hazards at the SFVF is inherently challenging due to uncertainties in the spatio-temporal distribution of future eruptive vents and the strong sensitivity of flow trajectories to subtle variations in slope and aspect, particularly on the gentle terrain.

To this aim we combined a remote sensing and numerical modeling approach to constrain the emplacement dynamics of the SP lava flow, and perform lava flow simulations on gentle slopes. We first analyze a high-resolution drone-derived digital elevation model (DEM) and orthomosaic to map lava flow outlines, surface structures (e.g., channels, levees, and folds), and topographic features such as grabens and fluvial incisions. This mapping is complemented by automated surface-texture classification using Sentinel-2 multispectral satellite data, enabling reconstruction of a sequential emplacement history, comprising two main pulses of extrusion. These observations inform the reconstruction of the pre-eruptive surface, incorporating inferred tectonic and fluvial features.

The reconstructed surface is then compared to the present topography to estimate lava flow volume and thickness distributions, and to constrain model parameters for lava flow simulations. We use the MrLavaLoba model, which includes an inertia-like parameter well suited for simulating lava propagation on gentle slopes. Multiple eruptive scenarios, including single-pulse and two-pulse eruptions, are simulated and quantitatively compared with thickness distribution observed in our derived DEM.

Our results demonstrate that detailed reconstruction of pre-eruptive topography significantly improves model–data agreement for lava flows emplaced on gentle terrain. We propose a best-practice workflow for integrating remote sensing data and lava flow modeling in distributed volcanic fields, with direct implications for future lava flow hazard assessments at the SFVF and similar volcanic systems worldwide.