- NSF-PAR ID:
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- Proceedings of the National Academy of Sciences
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract. The annual area burned due to wildfires in the western United States (WUS) increased bymore than 300 % between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (SML) framework, SMLFire1.0, to model observed fire frequencies and sizes in 12 km × 12 km grid cells across the WUS. This framework is implemented using mixture density networks trained on a wide suite of input predictors. The modeled WUS fire frequency matches observations at both monthly (r=0.94) and annual (r=0.85) timescales, as do the monthly (r=0.90) and annual (r=0.88) area burned. Moreover, the modeled annual time series of both fire variables exhibit strong correlations (r≥0.6) with observations in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire-month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000 h dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly. They also highlight the power of ML-driven parameterizations for potential implementation in fire modules of dynamic global vegetation models (DGVMs) and earth system models (ESMs).more » « less
Abstract. In steep wildfire-burned terrains, intense rainfall can produce large runoff that can trigger highly destructive debris flows. However, the abilityto accurately characterize and forecast debris flow susceptibility in burned terrains using physics-based tools remains limited. Here, we augmentthe Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfiredebris flow susceptibility over a regional domain. We perform hindcast simulations using high-resolution weather-radar-derived precipitation andreanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric rivertriggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California's famous Highway 1. Compared to thebaseline, our burn scar simulation yields dramatic increases in total and peak discharge and shorter lags between rainfall onset and peakdischarge, consistent with streamflow observations at nearby US Geological Survey (USGS) streamflow gage sites. For the 404 catchments located inthe simulated burn scar area, median catchment-area-normalized peak discharge increases by ∼ 450 % compared to the baseline. Catchmentswith anomalously high catchment-area-normalized peak discharge correspond well with post-event field-based and remotely sensed debris flowobservations. We suggest that our regional postfire debris flow susceptibility analysis demonstrates WRF-Hydro as a compelling new physics-basedtool whose utility could be further extended via coupling to sediment erosion and transport models and/or ensemble-based operational weatherforecasts. Given the high-fidelity performance of our augmented version of WRF-Hydro, as well as its potential usage in probabilistic hazardforecasts, we argue for its continued development and application in postfire hydrologic and natural hazard assessments.more » « less
Circum-boreal and -tundra systems are crucial carbon pools that are experiencing amplified warming and are at risk of increasing wildfire activity. Changes in wildfire activity have broad implications for vegetation dynamics, underlying permafrost soils, and ultimately, carbon cycling. However, understanding wildfire effects on biophysical processes across eastern Siberian taiga and tundra remains challenging because of the lack of an easily accessible annual fire perimeter database and underestimation of area burned by MODIS satellite imagery. To better understand wildfire dynamics over the last 20 years in this region, we mapped area burned, generated a fire perimeter database, and characterized fire regimes across eight ecozones spanning 7.8 million km2of eastern Siberian taiga and tundra from ∼61–72.5° N and 100° E–176° W using long-term satellite data from Landsat, processed via Google Earth Engine. We generated composite images for the annual growing season (May–September), which allowed mitigation of missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. The annual dNBR images were converted to binary burned or unburned imagery that was used to vectorize fire perimeters. We mapped 22 091 fires burning 152 million hectares (Mha) over 20 years. Although 2003 was the largest fire year on record, 2020 was an exceptional fire year for four of the northeastern ecozones resulting in substantial increases in fire activity above the Arctic Circle. Increases in fire extent, severity, and frequency with continued climate warming will impact vegetation and permafrost dynamics with increased likelihood of irreversible permafrost thaw that leads to increased carbon release and/or conversion of forest to shrublands.
Much of the precipitation delivered to western North America arrives during the cool season via midlatitude Pacific storm tracks, which may experience future shifts in response to climate change. Here, we assess the sensitivity of the hydroclimate and ecosystems of western North America to the latitudinal position of cool‐season Pacific storm tracks. We calculated correlations between storm track variability and three hydroclimatic variables: gridded cool‐season standardized precipitation‐evapotranspiration index, April snow water equivalent, and water year streamflow from a network of
USGSstream gauges. To assess how historical storm track variability affected ecosystem processes, we derived forest growth estimates from a large network of tree‐ring widths and land surface phenology and wildfire estimates from remote sensing. From 1980 to 2014, cool‐season storm tracks entered western North America between approximately 41°N and 53°N. Cool‐season moisture supply and snowpack responded strongly to storm track position, with positive correlations to storm track latitude in eastern Alaska and northwestern Canada but negative correlations in the northwestern U.S. Ecosystems of the western United States were greener and more productive following winters with south‐shifted storm tracks, while Canadian ecosystems were greener in years when the cool‐season storm track was shifted to the north. On average, larger areas of the northwestern United States were burned by moderate to high severity wildfires when storm tracks were displaced north, and the average burn area per fire also tended to be higher in years with north‐shifted storm tracks. These results suggest that projected shifts of Pacific storm tracks over the 21st century would likely alter hydroclimatic and ecological regimes in western North America, particularly in the northwestern United States, where moisture supply and ecosystem processes are highly sensitive to the position of cool‐season storm tracks.
Global changes and associated droughts, heat waves, logging activities, and forest fragmentation may intensify fires in Amazonia by altering forest microclimate and fuel dynamics. To isolate the effects of fuel loads on fire behavior and fire‐induced changes in forest carbon cycling, we manipulated fine fuel loads in a fire experiment located in southeast Amazonia. We predicted that a 50% increase in fine fuel loads would disproportionally increase fire intensity and severity (i.e., tree mortality and losses in carbon stocks) due to multiplicative effects of fine fuel loads on the rate of fire spread, fuel consumption, and burned area. The experiment followed a fully replicated randomized block design (
N= 6) comprised of unburned control plots and burned plots that were treated with and without fine fuel additions. The fuel addition treatment significantly increased burned area (+22%) and consequently canopy openness (+10%), fine fuel combustion (+5%), and mortality of individuals ≥5 cm in diameter at breast height (dbh; +37%). Surprisingly, we observed nonsignificant effects of the fuel addition treatment on fireline intensity, and no significant differences among the three treatments for (i) mortality of large trees (≥30 cm dbh), (ii) aboveground forest carbon stocks, and (iii) soil respiration. It was also surprising that postfire tree growth and wood increment were higher in the burned plots treated with fuels than in the unburned control. These results suggest that (i) fine fuel load accumulation increases the likelihood of larger understory fires and (ii) single, low‐intensity fires weakly influence carbon cycling of this primary neotropical forest, although delayed postfire mortality of large trees may lower carbon stocks over the long term. Overall, our findings indicate that increased fine fuel loads alone are unlikely to create threshold conditions for high‐intensity, catastrophic fires during nondrought years.