Fire affects soil and vegetation, which in turn can promote the initiation and growth of runoff-generated debris flows in steep watersheds. Postfire hazard assessments often focus on identifying the most likely watersheds to produce debris flows, quantifying rainfall intensity-duration thresholds for debris flow initiation, and estimating the volume of potential debris flows. This work seeks to expand on such analyses and forecast downstream debris flow runout and peak flow depth. Here, we report on a high fidelity computational framework that enables debris flow simulation over two watersheds and the downstream alluvial fan, although at significant computational cost. We also develop a Gaussian Process surrogate model, allowing for rapid prediction of simulator outputs for untested scenarios. We utilize this framework to explore model sensitivity to rainfall intensity and sediment availability as well as parameters associated with saturated hydraulic conductivity, hydraulic roughness, grain size, and sediment entrainment. Simulation results are most sensitive to peak rainfall intensity and hydraulic roughness. We further use this approach to examine variations in debris flow inundation patterns at different stages of postfire recovery. Sensitivity analysis indicates that constraints on temporal changes in hydraulic roughness, saturated hydraulic conductivity, and grain size following fire would be particularly beneficial for forecasting debris flow runout throughout the postfire recovery period. The emulator methodology presented here also provides a means to compute the probability of a debris flow inundating a specific downstream region, consequent to a forecast or design rainstorm. This workflow could be employed in scenario-based planning for postfire hazard mitigation.

ICESat-2, the first photon counting satellite, maps the earth’s surface with unprecedented details and accuracy. However, the resulting photon clouds, distributed as the ATL-03 geocoded photon product, are vast, unstructured, and noisy datasets. The high density and four-dimensional nature of the photon dataset (location and time), coupled with different responses over different surfaces (e.g., ice, forest cover, water), pose a unique and challenging problem regarding surface detection’s overall objective of intelligently reducing the data volume. Multiscale models uncover hidden structures in data due to their ability to analyze the underlying processes at multiple scales. Besides the traditional wisdom of using multiple scales for improving local and global approximations, in this work, we show their application as an intelligent sampling mechanism for redundant and noisy datasets. Our proposed approach’s fundamental idea is the generation of data dependent and multiscale basis functions and corresponding representative sparse representations, which retain points essential for minimizing the error of reconstruction. Thereby, points associated with rapid spatial change are chosen, while those that are easily reconstructed using the smooth basis functions are discarded. As the final output, the algorithm provides an efficient sparse representation of the data that captures all relevant features for modeling and prediction with quantified uncertainty. Our presentation includes a detailed description of the algorithm and theory as applied to process the ATL-03 geolocated photon product of the ICESat-2 mission. We will demonstrate the efficiency of our approach by examples of different ice sheet surfaces, including heavily crevassed glaciers, that pose a challenge for currently used change detection methods.

We present two models using monitoring data in the production of volcanic eruption forecasts. The first model enhances the well-established failure forecast method introducing an SDE in its formulation. In particular, we developed new method for performing short-term eruption timing probability forecasts, when the eruption onset is well represented by a model of a significant rupture of materials. The method enhances the well-known failure forecast method equation. We allow random excursions from the classical solutions. This provides probabilistic forecasts instead of deterministic predictions, giving the user critical insight into a range of failure or eruption dates. Using the new method, we describe an assessment of failure time on present-day unrest signals at Campi Flegrei caldera (Italy) using either seismic count and ground deformation data. The new formulation enables the estimation on decade-long time windows of data, locally including the effects of variable dynamics. The second model establishes a simple method to update prior vent opening spatial maps. The prior reproduces the two-dimensional distribution of past vent distribution with a Gaussian Field. The likelihood relies on a one-dimensional variable characterizing the chance of material failure locally, based, for instance, on the horizontal ground deformation. In other terms, we introduce a new framework for performing short-term eruption spatial forecasts by assimilating monitoring signals into a prior (“background”) vent opening map. To describe the new approach, first we summarize the uncertainty affecting a vent opening map pdf of Campi Flegrei by defining an appropriate Gaussian random field that replicates it. Then we define a new interpolation method based on multiple points of central symmetry, and we apply it on discrete GPS data. Finally, we describe an application of the Bayes’ theorem that combines the prior vent opening map and the data-based likelihood product-wise. We provide examples based on either seismic count and interpolated ground deformation data collected in the Campi Flegrei volcanic area.

Episodes of slow uplift and subsidence of the ground, called bradyseism, characterize the recent dynamics of the Campi Flegrei caldera (Italy). In the last decades two major bradyseismic crises occurred, in 1969/1972 and in 1982/1984, with a ground uplift of 1.70 m and 1.85 m, respectively. Thousands of earthquakes, with a maximum magnitude of 4.2, caused the partial evacuation of the town of Pozzuoli in October 1983. This was followed by about 20 years of overall subsidence, about 1 m in total, until 2005. After 2005 the Campi Flegrei caldera has been rising again, with a slower rate, and a total maximum vertical displacement in the central area of ca. 70 cm. The two signals of ground deformation and background seismicity have been found to share similar accelerating trends. The failure forecast method can provide a first assessment of failure time on present‐day unrest signals at Campi Flegrei caldera based on the monitoring data collected in [2011, 2020] and under the assumption to extrapolate such a trend into the future. In this study, we apply a probabilistic approach that enhances the well‐established method by incorporating stochastic perturbations in the linearized equations. The stochastic formulation enables the processing of decade‐long time windows of data, including the effects of variable dynamics that characterize the unrest. We provide temporal forecasts with uncertainty quantification, potentially indicative of eruption dates. The basis of the failure forecast method is a fundamental law for failing materials: ẇ-α ẅ = A, where ẇ is the rate of the precursor signal, and α, A are model parameters that we fit on the data. The solution when α >1 is a power law of exponent 1/(1 − α) diverging at time Tf , called failure time. In our case study, Tf is the time when the accelerating signals collected at Campi Flegrei would diverge if we extrapolate their trend. The interpretation of Tf as the onset of a volcanic eruption is speculative. It is important to note that future variations of monitoring data could either slow down the increase so far observed, or suddenly further increase it leading to shorter failure times than those here reported. Data from observations at all locations in the region were also aggregated to reinforce the computations of Tf reducing the impact of observation errors.