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Abstract Forest fire frequency, extent, and severity have rapidly increased in recent decades across the western United States (US) due to climate change and suppression‐oriented wildfire management. Fuels reduction treatments are an increasingly popular management tool, as evidenced by California's plan to treat 1 million acres annually by 2050. However, the aggregate efficacy of fuels treatments in dry forests at regional and multi‐decadal scales is unknown. We develop a novel fuels treatment module within a coupled dynamic vegetation and fire model to study the effects of dead biomass removal from forests in the Sierra Nevada region of California. We ask how annual treatment extent, stand‐level treatment intensiveness, and spatial treatment placement alter fire severity and live carbon loss. We find that a ∼30% reduction in stand‐replacing fire was achieved under our baseline treatment scenario of 1,000 km² year⁻¹after a 100‐year treatment period. Prioritizing the most fuel‐heavy stands based on precise fuel distributions yielded cumulative reductions in pyrogenic stand‐replacement of up to 50%. Both removing constraints on treatment location due to remoteness, topography, and management jurisdiction and prioritizing the most fuel‐heavy stands yielded the highest stand‐replacement rate reduction of ∼90%. Even treatments that succeeded in lowering aggregate fire severity often took multiple decades to yield measurable effects, and avoided live carbon loss remained negligible across scenarios. Our results suggest that strategically placed fuels treatments are a promising tool for controlling forest fire severity at regional, multi‐decadal scales, but may be less effective for mitigating live carbon losses. 

Abstract. Climate change and increased fire are eroding theresilience of boreal forests. This is problematic because boreal vegetationand the cold soils underneath store approximately 30 % of all terrestrialcarbon. Society urgently needs projections of where, when, and why borealforests are likely to change. Permafrost (i.e., subsurface material thatremains frozen for at least 2 consecutive years) and the thicksoil-surface organic layers (SOLs) that insulate permafrost are importantcontrols of boreal forest dynamics and carbon cycling. However, both arerarely included in process-based vegetation models used to simulate futureecosystem trajectories. To address this challenge, we developed acomputationally efficient permafrost and SOL module named the Permafrost andOrganic LayEr module for Forest Models (POLE-FM) that operates at finespatial (1 ha) and temporal (daily) resolutions. The module mechanisticallysimulates daily changes in depth to permafrost, annual SOL accumulation, andtheir complex effects on boreal forest structure and functions. We coupledthe module to an established forest landscape model, iLand, and benchmarkedthe model in interior Alaska at spatial scales of stands (1 ha) tolandscapes (61 000 ha) and over temporal scales of days to centuries. Thecoupled model generated intra- and inter-annual patterns of snowaccumulation and active layer depth (portion of soil column that thawsthroughout the year) generally consistent with independent observations in17 instrumented forest stands. The model also represented the distributionof near-surface permafrost presence in a topographically complex landscape.We simulated 39.3 % of forested area in the landscape as underlain bypermafrost, compared to the estimated 33.4 % from the benchmarkingproduct. We further determined that the model could accurately simulate mossbiomass, SOL accumulation, fire activity, tree species composition, andstand structure at the landscape scale. Modular and flexible representationsof key biophysical processes that underpin 21st-century ecologicalchange are an essential next step in vegetation simulation to reduceuncertainty in future projections and to support innovative environmentaldecision-making. We show that coupling a new permafrost and SOL module to anexisting forest landscape model increases the model's utility for projectingforest futures at high latitudes. Process-based models that representrelevant dynamics will catalyze opportunities to address previouslyintractable questions about boreal forest resilience, biogeochemicalcycling, and feedbacks to regional and global climate. 

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). 

Abstract Escalating burned area in western US forests punctuated by the 2020 fire season has heightened the need to explore near-term macroscale forest-fire area trajectories. As fires remove fuels for subsequent fires, feedbacks may impose constraints on the otherwise climate-driven trend of increasing forest-fire area. Here, we test how fire-fuel feedbacks moderate near-term (2021–2050) climate-driven increases in forest-fire area across the western US. Assuming constant fuels, climate–fire models project a doubling of  forest-fire area compared to 1991–2020. Fire-fuel feedbacks only modestly attenuate the projected increase in forest-fire area. Even models with strong feedbacks project increasing interannual variability in forest-fire area and more than a two-fold increase in the likelihood of years exceeding the 2020 fire season. Fuel limitations from fire-fuel feedbacks are unlikely to strongly constrain the profound climate-driven broad-scale increases in forest-fire area by the mid-21st century, highlighting the need for proactive adaptation to increased western US forest-fire impacts. 

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs ( P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water supplies and runoff-related risks. 

Abstract Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways towards mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.