Recipient Organization
SPATIAL INFORMATICS GROUP LLC
2529 YOLANDA CT
PLEASANTON,CA 945667513
Performing Department
(N/A)
Non Technical Summary
This proposal addresses the SBIR Phase I topic area, "Forests and Related Resources - Topic Area 8.1." Specifically, it aims to "address the health, diversity and productivity of the Nation's forest and grasslands... [and] improve sustainability of forest resources," where "new technologies are needed to enhance the protection of the Nation's forested lands and forest resources and help to ensure the continued existence of healthy and productive forest ecosystems." It addresses the program's long-term goals of achieving healthy and sustainable forest ecosystems that are more resilient to wildfires and the research priority "Developing Technology that Facilitates the Management of Wildfire on Public Lands" with "research that provides systems for ... managing wildfires; systems for reducing fuel loads in forests; tools and equipment for improving the efficacy and safety of firefighters on the ground and in the air."Forest management today is exceedingly complex, a key difficulty being assessing and managing uncertainty to limit risk. It aims to mitigate the extent and impact of wildfires that are said to be becoming more unpredictable and which are often characterized by complex, uncertain behavior in steep, complex, forested terrain. This is weighed against strategic manipulation of forest fuel amount and structure using tools such as prescribed fire, which is accompanied by its own risks. For example, prescribed fires that unfolded badly include Colorado's Lower North Fork Fire, in which a prescribed fire killed a civilian, and California's Lowden Ranch Fire, which grew to over 800 ha and destroyed 23 homes. Retrospectively, inadequate evaluation of fire and weather conditions is often cited in such cases. Uncertainty about the smoke impacts of prescribed fire also reduces its use. Mitigation now requires quantitative planning, but tools do not currently have the dynamic complexity and means to estimate confidence in predicted fire behavior, smoke, and ecological effects, especially in complex environments. To date, planning has used deterministic kinematic models--and thus ignores the potential for fire-induced winds, dynamic feedbacks, and amplification--and also uses oversimplified meteorological circulations; thus, it can underestimate the range of possible outcomes and misinterpret the impact of management activities. The impacts of better estimating uncertainty extend to improving firefighter safety, as entrapment potential peaks when fire behavior rapidly deviates from an assumed trajectory and becomes extreme (Page et al. 2019).This work is appropriate for a Phase I SBIR because it will demonstrate the technical feasibility of the proposed framework and bring the innovation closer to commercialization by completing the fundamental research and evaluation and demonstrating outcomes in several different types of fires. The innovation involves a high degree of risk, in that our hypothesis that by introducing an uncertainty-based range of primary environmental variables to a modeling system, uncertainty in outcomes can be estimated is untested and the spread of simulations could therefore underestimate natural variability; this is a hurdle that the USDA-funded R&D is intended to overcome.
Animal Health Component
20%
Research Effort Categories
Basic
10%
Applied
20%
Developmental
70%
Goals / Objectives
Our goal is to develop, apply, and distribute a framework for conducting, evaluating, and communicating probabilistic fire growth forecasts--an approach that can be used with a wide range of fire behavior modeling systems across several economic sectors. This approach will transmit the uncertainty in key fire environment input variables--notably, weather and fuel loads, which are explored here (the approach can be applied to other variables)--in order to inform stakeholders about the uncertainty in predicted outcomes of fire behavior and effects (e.g., smoke production and fire severity). It will develop new methods to evaluate probabilistic fire behavior forecasts--a different challenge than verifying a single deterministic forecast. An important research component is developing methods to graphically and meaningfully communicate probabilistic fire growth information expressing forecast uncertainty to a wide range of stakeholders, several of which have committed letters of support/collaboration.Objective 1: Develop methods to conduct probabilistic prediction of fire behavior and effects using ensembles of fire behavior simulations--here, coupled weather-fire behavior model simulations using the CAWFE model--where the spread of an input variable represents the uncertainty in its state and spread of outcomes represents the consequent uncertainty in fire behavior and effects (analogous to Whitaker and Loughe [1998]). Demonstrate ensemble design and simulation on select wildfire events.For the key input of weather, use existing ensemble numerical weather prediction forecasts to represent uncertainty in the weather and related dead fuel moisture content.Use new fuel databases that provide distributions of fuel loads and other properties to create an ensemble of simulations for select wildland fire events where the spread in fire behavior and effects outcomes reveals our possible error in fire behavior due to errors in measurement or representation of fuel characteristics.Objective 2: Create and apply improved methods to evaluate and communicate the accuracy of probabilistic fire behavior forecasts. Explore with stakeholders.Objective 3: Improve the dissemination of probabilistic fire growth forecasts by creating new graphical representations and communication methods to express the most likely outcomes, the spread of solutions, occurrence of outliers, and other possible products. Refine with stakeholders
Project Methods
In Phase I, we propose activities to (i) develop a framework to conduct probabilistic fire behavior predictions, (ii) demonstrate it using two primary sources of uncertainty (weather forecast and fuel loads), and (iii) develop and make available better methods to evaluate, display, and communicate how these sources of uncertainty culminate in a range of possible prescribed and wildfire behavior and fire effects. This work will be done with the aim of developing methods and business services products with stakeholders who produce or use fire model forecasts, from the USDA Forest Service, CAL FIRE, and Pyregence.org.Task 1: Select at least four recent wildfire events that represent a range of behavior types (e.g., both wind-driven and plume-driven), have community interest, and are among archived datasets or occur before the project start.In ensemble weather prediction, ensemble spread can depend on the flow and characteristics of topography. Ensemble spread tends to be large in weak forcing scenarios and smaller when forcing is strong. Thus, we will examine the outcomes of simulated fires in different types of environments to see whether certain types/drivers of fires have different spread characteristics (e.g., wind-driven fires vs. plume-driven fires that create winds and propagate themselves).Task 2: Construct ensembles. In this research study, we will use existing probabilistic weather forecast ensembles and probabilistic fuel information to initialize coupled weather-fire behavior simulations.2a: Conduct ensemble simulations of each selected fire event using NOAA ensemble weather forecasts as input, to produce a range of possible fire behavior outcomes.Fire spread is acutely sensitive to wind and, in turn, fire shapes the wind and further fire growth through fire-atmosphere interactions. Thus, weather uncertainty is a primary uncertainty source. Weather uncertainty will be considered by providing CAWFE with varied initial and boundary conditions from one or both ready-made NCEP forecast ensembles including the Short-Range Ensemble Forecast (SREF) (21 members, 16 km grid spacing, hourly output to 39 h and 3-hourly output to 87 h) and the Global Ensemble Forecast System (GEFS) (21 members, 0.5 degree grid spacing, output to 16 days), each of which is initialized 4x per day. We will consider initializing CAWFE meteorology with storm-scale ensembles, such as the 3 km HRRR-E. (Investigators' experience suggests this may or may not be helpful.) As CAWFE interactive grid refinement nests simulations down to convective scales (hundreds of meters), we anticipate the ensemble will produce a range of motions that in turn will result in a range of fire behaviors and effects, the spread of which will grow throughout the length of the forecast. A CAWFE simulation could conceivably continue until the end of the large-scale forecast (to 87 h or 16 days, using SREF or GEFS output, respectively) but the rapid accumulation of model error, particularly when modeling at these fine scales, limits each forecast simulation to a few days. We will draw from our archived SREF forecasts for several past events and will also use significant events of interest to partners that have yet to occur.2b: Conduct ensemble simulations of each selected fire event using NAWFD fuel load probability distributions to construct a range of possible fuel loads as input in order to produce a range of possible fire behavior outcomes.We will investigate the impact of uncertainty in fuels (which arises from measurement or methodological errors in estimating properties--notably fuel load) on modeled fire outcomes. Unlike previous work, in this study the range of inputs will not span all possible natural variability but will be based on NAWFD which assembles data from many sources, its uncertainty estimates, to estimate their subsequent impacts on modeled fire behavior.Task 3: Conduct ensemble simulations of the cases identified in Task 1.To conduct a simulation, in addition to the NCEP large-scale gridded weather forecast and fuel load data for various components from NAWFD, additional data is gathered including terrain data at 30, 3, and 1 arc second, and information on fire ignition time and location or a spatial fire perimeter if already in progress. Fuel moistures will be drawn from USFS databases and/or Remote Automated Weather Stations. (Investigation of uncertainty due to fuel moisture, although among the important factors, is left to Phase II.) The large-scale gridded forecast is projected onto the CAWFE model outer domain grid points (~10 km spacing) to initialize the model, and later periods in the NCEP forecast provide evolving lateral boundary conditions. The simulation telescopes in with finer resolution domains (3.3 km horizontal grid spacing, 1.1 km, and 370 m, with accompanying vertical grid refinement). At the ignition or observation time, the fire ignition or perimeter is inserted into the running weather model and the simulation, with interacting fire and weather, is carried out throughout the forecast period at time steps of approximately a second in the innermost domain, with model and fire state information produced at minute intervals for our analysis.We will demonstrate our developments using the CAWFE model because coupled models have the ability to simulate dynamic interactions and, thus, unanticipated amplification of fire behavior, including the parameter space where outlier events occur; however, the results can be applied to--and would improve--any fire behavior modeling system. The interpretation is that the spread of an input variable represents the uncertainty in its state and that the spread of outcomes represents the consequent uncertainty in fire behavior and effects.Due to limited resources, we will begin with separate ensembles that vary weather and fuel inputs (the weather-varying ensemble using the fuel mean data and the fuel-varying ensemble using the ensemble unperturbed central member). Then conditional probability methods would be a simple approach to combine probability scores from both weather-varying and fuel-varying ensembles.Task 4: Create new graphical representations and communication methods to express the most likely outcomes, the spread of solutions, occurrence of outliers, and other possible products, and explore options with stakeholders.Spatial fire growth probabilities might be expressed a few different ways. Outcomes will be expressed in terms of point probability, for example, whether a particular point will be overrun by fire during the forecast, or one might express a probability regarding how much area is burned.Task 5: Develop and apply improved methods to the ensembles of simulations to evaluate and communicate the accuracy of probabilistic fire behavior forecasts.The probability of whether a fire reaching a point can be determined explicitly from ensemble output, and timing and spatial errors will be objectively determined. Forecasts of fire extent will be compared against available observations of fire extent as measured from satellites, airborne infrared instruments, or incident information. Spatial analysis of the ensemble of CAWFE simulations would first estimate the probability a location is overrun by a fire during the length of the forecast vs. the frequency it occurs in simulations.We will explore methods to estimate ensemble forecast accuracy and seek feedback with stakeholders. It is anticipated that dynamic ensemble forecasts should be able to best FSPro predictions based on climatology, because past weather is not a good predictor of the future.