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Mountain environments are profoundly impacted by the deposition of mineral dust, yet the degree to which this material is far-traveled or intra-regional is typically unclear. This distinction is fundamental to model future changes in mountain geoecosystems resulting from climatic or anthropogenic forcing in dust source regions. We address this question with a network of 17 passive dust samplers installed in primarily mountain locations in Utah, Nevada, and Idaho between October, 2020 and October 2021. For each collector, the dust deposition rate was calculated, and the physical and chemical properties of the dust were constrained. Results were combined with backward trajectory modeling to identify the geologic characteristics of the area over which air passed most frequently in route to each collector (the ‘hot spot’). Dust properties differ significantly between collectors, hot spots for many collectors are spatially discrete, and the dominant geologies in the hot spots corresponding to each collector vary considerably. These results support the hypothesis that the majority of the dust deposited in the areas we studied is sourced from arid lowlands in the surrounding region.

 

Seasonal snowmelt from the Wasatch Mountains of northern Utah, USA, is a primary control on water availability for the metropolitan Wasatch Front, surrounding agricultural valleys, and the Great Salt Lake (GSL). Prolonged drought, increased evaporation due to warming temperatures, and sustained agricultural and domestic water consumption have caused GSL water levels to reach record low stands in 2021 and 2022, resulting in increased exposure of dry lakebed sediment. When dust emitted from the GSL dry lakebed is deposited on the adjacent Wasatch snowpack, the snow is darkened, and snowmelt is accelerated. Regular observations of dust-on-snow (DOS) began in the Wasatch Mountains in 2009, and the 2022 season was notable for both having the most dust deposition events and the highest snowpack dust concentrations. To understand if record high DOS concentrations were linked to record low GSL levels, dust source regions for each dust event were identified through a backward trajectory model analysis combined with aerosol measurements and field observations. Backward trajectories indicated that the exposed lakebed of the GSL contributed 23% of total dust deposition and had the highest dust emissions per surface area. The other potential primary contributors were the GSL Desert (45%) and the Sevier +Tule dry lakebeds (17%), both with lower per-area emissions. The impact on snowmelt, quantified by mass and energy balance modeling in the presence and absence of snow darkening by dust, was over 2 weeks (17 d) earlier. The impact of dust on snowmelt could have been more dramatic if the spring had been drier, but frequent snowfall buried dust layers, delaying dust-accelerated snowmelt later into the melt season.

 

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was <3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM 2 . 5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%. 

A microscale wildfire model, QES-Fire, that dynamically couples the fire front to microscale winds was developed using a simplified physics rate of spread (ROS) model, a kinematic plume-rise model and a mass-consistent wind solver. The model is three-dimensional and couples fire heat fluxes to the wind field while being more computationally efficient than other coupled models. The plume-rise model calculates a potential velocity field scaled by the ROS model’s fire heat flux. Distinct plumes are merged using a multiscale plume-merging methodology that can efficiently represent complex fire fronts. The plume velocity is then superimposed on the ambient winds and the wind solver enforces conservation of mass on the combined field, which is then fed into the ROS model and iterated on until convergence. QES-Fire’s ability to represent plume rise is evaluated by comparing its results with those from an atmospheric large-eddy simulation (LES) model. Additionally, the model is compared with data from the FireFlux II field experiment. QES-Fire agrees well with both the LES and field experiment data, with domain-integrated buoyancy fluxes differing by less than 17% between LES and QES-Fire and less than a 10% difference in the ROS between QES-Fire and FireFlux II data. 

Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progression is through the fire arrival time, which defines the time that the fire arrives at a given location. Estimating the fire arrival time is critical for initializing the fire location within coupled fire-atmosphere models. We present a new method that utilizes machine learning to estimate the fire arrival time from satellite data in the form of burning/not burning/no data rasters. The proposed method, based on a support vector machine (SVM), is tested on the 10 largest California wildfires of the 2020 fire season, and evaluated using independent observed data from airborne infrared (IR) fire perimeters. The SVM method results indicate a good agreement with airborne fire observations in terms of the fire growth and a spatial representation of the fire extent. A 12% burned area absolute percentage error, a 5% total burned area mean percentage error, a 0.21 False Alarm Ratio average, a 0.86 Probability of Detection average, and a 0.82 Sørensen’s coefficient average suggest that this method can be used to monitor wildfires in near-real-time and provide accurate fire arrival times for improving fire modeling even in the absence of IR fire perimeters. 

Forecasting fire growth, plume rise and smoke impacts on air quality remains a challenging task. Wildland fires dynamically interact with the atmosphere, which can impact fire behavior, plume rises, and smoke dispersion. For understory fires, the fire propagation is driven by winds attenuated by the forest canopy. However, most numerical weather prediction models providing meteorological forcing for fire models are unable to resolve canopy winds. In this study, an improved canopy model parameterization was implemented within a coupled fire-atmosphere model (WRF-SFIRE) to simulate a prescribed burn within a forested plot. Simulations with and without a canopy wind model were generated to determine the sensitivity of fire growth, plume rise, and smoke dispersion to canopy effects on near-surface wind flow. Results presented here found strong linkages between the simulated fire rate of spread, heat release and smoke plume evolution. The standard WRF-SFIRE configuration, which uses a logarithmic interpolation to estimate sub-canopy winds, overestimated wind speeds (by a factor 2), fire growth rates and plume rise heights. WRF-SFIRE simulations that implemented a canopy model based on a non-dimensional wind profile, saw significant improvements in sub-canopy winds, fire growth rates and smoke dispersion when evaluated with observations. 

We present an interactive HPC framework for coupled fire and weather simulations. The system is suitable for urgent simulations and forecast of wildfire propagation and smoke. It does not require expert knowledge to set up and run the forecasts. The core of the system is a coupled weather, wildland fire, fuel moisture, and smoke model, running in an interactive workflow and data management system. The system automates job setup, data acquisition, preprocessing, and simulation on an HPC cluster. It provides animated visualization of the results on a dedicated mapping portal in the cloud, and as GIS files or Google Earth KML files. The system also serves as an extensible framework for further research, including data assimilation and applications of machine learning to initialize the simulations from satellite data. Index Terms—WRF-SFIRE, coupled atmosphere-fire model, MODIS, VIIRS, satellite data, fire arrival time, data assimilation, machine learning 

During the summer of 2015, a number of wildfires fires burned across northern California, which produced significant smoke across the region. Smoke from these wildfires hindered fire-fighting efforts by delaying helicopter operations and exposed communities to high concentrations of atmospheric pollutants. Nighttime inversions are common across the western U.S. and usually mix out during the early afternoon as a result of convective mixing from daytime heating. However, atmospheric conditions in valleys adjacent to the aforementioned wildfires remained stable throughout the afternoon. It is hypothesized that the smoke from nearby wildfires enhanced atmospheric stability due to surface cooling caused by reduced incoming solar radiation, and possibly by warming aloft due to absorption of the incoming solar radiation in the smoke layer. At the same time, mid-level heating from the wildfire could have increased atmospheric stability and extended the duration of the inversion. In this study, we utilize the WRF-SFIRE-CHEM modeling framework, which couples an atmospheric, chemical, and fire spread model in an effort the model the impacts of smoke on local inversions and to improve the physical understanding behind these smoke-induced inversion episodes. This modeling framework was used to simulate the Route and South Complex fires between August 10 – August 26th, 2015. Preliminary results indicate that wildfire smoke may have significantly reduced incoming solar radiation, leading to local surface cooling by up to 2-3 degrees. Direct heating from the fire itself does not significantly enhance atmospheric stability. However, mid-level warming was observed in the smoke layer suggesting that absorption in this layer may have enhanced the inversion. This study suggests the including the fire-smoke- atmosphere feedbacks in a coupled modeling framework such as WRF-SFIRE-CHEM may help in capturing the impacts of wildfire smoke on near-surface stability and local inversions.