This project responds to the Strategic Environmental Research and Development Program (SERDP)’s FY 2020 Statement of Need: “DoD WILDLAND FIRE MANAGEMENT RESEARCH FOR IMPROVED MILITARY LAND USE”, for which the overarching objective was “to improve understanding of self-organization of convective structures and near-fire smoke plume development for the purpose of ultimately improving the management of fire for military land-use”. Our project addresses this overarching objective through a combination of robust science data collection on experimental fires, expansion of modeling capabilities, and detailed physics-based simulation modeling. Collectively, by closing gaps in measurements, understanding and modeling capabilities, our project seeks to close gaps between research and management, offering a path to substantially advance science-based prescribed fire management.
Prescribed fire is used extensively on DoD lands to proactively manage fuels, reduce risk, and improve control of wildland fires, but management burns are often greatly constrained by concerns about downwind smoke concentrations in populated areas. Experienced prescribed fire managers can manipulate smoke accumulations through ignition patterns that increase fire intensity, often causing coalescence of multiple plume cores into fewer, more coherent plumes, increasing plume rise, and reducing local smoke as emissions are transported farther and dispersed better. However, increased intensity often makes control more difficult and increases fire severity; prescribed fire managers need improved understanding of these tradeoffs to meet their management objectives. While recently funded SERDP projects have addressed many aspects important to fire modeling, such as fuels and fire measurements, gaps in measurements arise, where characteristics such as the plume rise, dimensions of the plume envelope, and eventual stabilization height, are often not measured. Data are frequently collected high in the plume with aircraft, and close to the ground with instruments, but not in-between, leading to gaps in scales of observation. This is particularly true for data characterizing smoke composition within the near field development zone, where concentrations of initial chemical species formed provide key insights for secondary species formed in subsequent reactions, such as ozone. Such gaps in measurements are problematic: without sufficient measurements of the smoke plume, observational datasets intended to connect ignition patterns, fuel characteristics, and fire behavior to plume development will be incomplete and cannot support the testing of plume models. Fundamentally, model evaluation efforts require integrated datasets which characterize fuels, fire, smoke, and plumes in space and time in a coherent framework. At present, fully integrated data is extremely limited, despite several large field collection efforts in recent years (Mell and Linn 2017). However, new technologies such as UAS show promise in closing these gaps, with unparalleled flexibility in their scale of observation and the capacity to collect data in places inaccessible to larger aircraft, booms or towers. While use of UAS in research is growing rapidly, they have not yet been used on experimental fires to their full potential.
In addition to gaps in measurements, gaps in understanding add uncertainty to management decisions. For example, live fuel moisture content (LFMC) can greatly affect fire behavior, especially under dry conditions (Pimont et al 2019a, 2019b; Martin-St Paul et al 2018), but LFMC effects on fire intensity, emissions, plume rise and related dynamics are still largely unknown. Moreover, LFMC dynamics over time are not well understood, and at present, can only be measured from field based samples not practical at landscape scales, and temporal dynamics of LFMC assessed through drought indices (e.g. Keetch-Byram Drought Index) are not strongly correlated (Ruffault et al 2018). Linking live fuel moisture content and fire behavior is an emerging, but promising approach, called Pyro-Ecophysiology (Jolly and Johnson 2018). Recent advances in plant physiology modelling now make it possible to quantify fuel moisture dynamics based on plant hydraulic traits, climate and forest stand characteristics (Martin-StPaul et al. 2017, Martin-StPaul et al 2018). In addition, the traits underlying these fuel moisture dynamics drive vulnerability of trees to drought related mortality (Choat et al. 2018), and thus provide an opportunity to more effectively model post-fire effects on surviving vegetation (Hood et al. 2018). Improved understanding of the timing and nature of live fuel moisture dynamics and fire effects that accounts for species and stand characteristics, will enable managers to better plan burn prescription windows and anticipate fire impacts on surviving vegetation. As climate change effects become more pronounced over time, robust understanding of these dynamics will be essential in developing management plans adapted to future drought events, to maintain resilient landscapes and ensure adequate control in prescribed fires.
As numerous factors affect fire intensity and plume dynamics in prescribed burns, modeling would ideally play a role in prescribed burn planning. At present, however, the gap between operational needs and modeling capabilities is significant; operational models are fast but do not address fire physical processes or plume dynamics, while “full” fire physics CFD-based fire models (e.g. FIRETEC , WFDS; Mell and Linn 2017) address these processes but are generally too computationally demanding for operational use. To close this gap, “reduced fire-physics” approaches are currently being developed by both the WFDS development team, with a “level set” approach (e.g. Bova et al 2016), called WFDS-LS, and by the FIRETEC development team, which employs a simpler CFD approach called QUIC-Fire. Both of these approaches reduce the complexity of the physics models and associated computational costs, enabling faster than real time calculations and providing insights regarding plume dynamics and smoke transport over much larger and more operationally relevant extents. One significant advantage of reduced fire-physics approaches is that, by essentially separating modeling of the plume from more detailed modeling of how fire spreads, they can be parameterized more simply, using only data characterizing winds, and the geometry and heat release rate of the fire front over time, an approach known as the “Burner method”, described in the FASMEE Study Plan (Ottmar et al 2017), and in Mell and Linn (2017). Such data can be collected in experimental burns, particularly with UAS-based infrared imagery, as proposed here, and provide a tractable path for evaluation of plume models in field conditions which can be very challenging with full fire-physics CFD-based fire models. These models characterize heat transfer processes, fuel thermal degradation, and associated dynamic fire behavior at high spatial and temporal detail, often far exceeding what is possible to measure. Conversely, high resolution imagery or terrestrial LiDAR can capture fuel heterogeneity at scales that are not practical to model; at present, techniques for translating the infinite detail of real world fuels to model-able quantities are limited and not standardized. This gap between real world fuels and fire models is a significant barrier as well. These gaps underline the need for efforts that connect the dots, both between simpler (“burner method”) and more complicated (full physics modeling) approaches, and also for connecting the dots between datasets and models. There is a benefit to building such connections, working out from the tractable intersections to greater understanding through sensitivity analysis, exploration of phenomenology, and hypothesis generation and testing. For such purposes it is essential to be able to marshal modeling capabilities at multiple scales and levels of process detail. There are also synergistic benefits from applying different models to the same problem; agreement between different models increases confidence that the underlying science is robust, and insights from different models may be complementary. Historically it has been challenging to run comparative simulations between multiple fire models due to significant differences in model architecture, required inputs, and output formats. Also, in most cases, such simulations have been project specific, and typically lack standardized approaches for developing input files or for post-processing simulation output. These factors make it difficult to evaluate these models with experimental data, and significantly impede progress in the development of modeling approaches that are tractable for management use. Recently, however, a 3D fuel and fire modeling platform was developed (STANDFIRE, Parsons et al 2018) that links the Forest Vegetation Simulator (Crookston and Dixon 2005), Fire and Fuels Extension (FFE-FVS, Reinhardt and Crookston 2003), an empirical forestry model used widely in the US in silvicultural and fuel treatment analysis, to the physics-based fire models WFDS and FIRETEC; by using the same fuels data in both fire models, STANDFIRE provides an opportunity for model comparisons. Additionally, such spatially explicit modeling can enable simulation analysis that tests the sensitivity to fuel variability and how that translates to uncertainty in fire behavior (Parsons et al 2017).
Close gaps in measurements, modeling and understanding, enabling more effective science-based management of DoD lands
- Collect integrated data sets of fuels, fire behavior, weather and smoke on Rx fires
- Expand modeling capabilities with STANDFIRE –capture real world fire events, represent with physics-based fire modeling
- Simulation modeling: multiple scales, drivers and models
- Integrated 3D space and time datasets of fuels, fire, weather, ignition, and plumes, suitable for use in model evaluation efforts
- Substantial expansion of modeling capabilities
- Facilitate model evaluations vs field experiments
- Incorporation of multiple fire models
- Incorporation of live fuel moisture content dynamics
- Science-developed VR prototype training platform for fire managers
- Minimum of 8 peer-reviewed papers
- Minimum of 10 presentations at conferences
6,000 acres are currently scheduled in 2021 as RX research burns.
Mapping and modeling fuels and fire at the Sycan Marsh, Oregon