Wildland fire modeling tools can ingest high resolution 3D vegetation models as inputs. However, data used to build the surface fuels in these models is often at a 30-meter resolution, which does not necessarily provide sufficient detail for accurate modeling of fires. Terrestrial laser scans are increasingly being used to collect detailed vegetation data that could be integrated with new approaches to fuel and fire modeling, but manual segmentation of scans is not scalable beyond a small number of scans. There is a need to automatically segment these high resolution point clouds as they are collected in the field, such that they may be leveraged by fuel and fire models for wildland fire response and mitigation and other applied climate science. This paper summarizes our early work on a labeling, visualization and machine learning pipeline for detailed segmentation of fuels. Specific contributions are: (1) a labeling approach involving 3 dimensional segmentation of point clouds using a point cloud processing engine; (2) a visualization approach using a computer graphics engine; and (3) early results from a deep learning modeling approach for fuel segmentation by category (live and dead) and size class (1, 10, 100 and 1000 hour fuels).

Long-standing fire suppression policies, global warming and human influence at the urban-wildland interface are fueling a global wildfire crisis. Prescribed burns are increasingly being recognized as an essential procedure to reduce fuel (biomass) overgrowth and mitigate the size and severity of uncontrolled wildfires. Next-generation three-dimensional (3D) fire behavior simulations, which can help land managers to identify risks and improve planning for successful prescribed burns, depend on accurate 3D fuel structure models. We introduce TrueTrees, a workflow that integrates tree-level observations from airborne lidar surveys into FastFuels 3D fuel models. The workflow is optimized and distributed to allow processing of point cloud data for typical burn units within minutes. TrueTrees is implemented into a prototype of BurnPro3D, a user-friendly science-driven decision support platform for prescribed burn planners.

Timely prediction of debris flow probabilities in areas impacted by wildfires is crucial to mitigate public exposure to this hazard during post-fire rainstorms. This paper presents a machine learning approach to amend an existing dataset of post-fire debris flow events with additional features reflecting existing vegetation type and geology, and train traditional and deep learning methods on a randomly selected subset of the data. The developed methods achieve AUC (area under the receiver operational characteristic curve) values of 0.93 (random forest) and 0.92 (neural network) on the test set, representing a significant improvement over a logistic regression model currently used (AUC 0.79). The paper also overviews a distributed, Kubernetesbased big data processing pipeline to efficiently retrieve features in areas impacted by new fires, and deploy the methods for real-time prediction of debris flow hazards.

The omission of nonlinear effects in large-scale 3D ground motion estimation, which are particularly challenging due to memory and scalability issues, can result in costly misguidance for structural design in earthquake-prone regions. We have implemented nonlinearity using a Drucker-Prager yield condition in AWP-ODC and further optimized the CUDA kernels to more efficiently utilize the GPU's memory bandwidth. The application has resulted in a significant increase in the model region and accuracy for state-of-the-art earthquake simulations in a realistic earth structure, which are now able to resolve the wavefield at frequencies relevant for the most vulnerable buildings (> 1 Hz) while maintaining the scalability and efficiency of the method. We successfully run the code on 4,200 Kepler K20X GPUs on NCSA Blue Waters and OLCF Titan to simulate a M 7.7 earthquake on the southern San Andreas fault with a spatial resolution of 25 m for frequencies up to 4 Hz.

Petascale simulations are needed to understand the rupture and wave dynamics of the largest earthquakes at shaking frequencies required to engineer safe structures (> 1 Hz). Toward this goal, we have developed a highly scalable, parallel application (AWP-ODC) that has achieved “M8”: a full dynamical simulation of a magnitude-8 earthquake on the southern San Andreas fault up to 2 Hz. M8 was calculated using a uniform mesh of 436 billion 40-m3cubes to represent the three-dimensional crustal structure of Southern California, in a 800 km by 400 km area, home to over 20 million people. This production run producing 360 sec of wave propagation sustained 220 Tflop/s for 24 hours on NCCS Jaguar using 223,074 cores. As the largest-ever earthquake simulation, M8 opens new territory for earthquake science and engineering - the physics-based modeling of the largest seismic hazards with the goal of reducing their potential for loss of life and property.