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Climate projections for the southwestern US suggest a warmer, drier future and have the potential to impact forest carbon (C) sequestration and post-fire C recovery. Restoring forest structure and surface fire regimes initially decreases total ecosystem carbon (TEC), but can stabilize the remaining C by moderating wildfire behavior. Previous research has demonstrated that fire maintained forests can store more C over time than fire suppressed forests in the presence of wildfire. However, because the climate future is uncertain, I sought to determine the efficacy of forest management to moderate fire behavior and its effect on forest C dynamics under current and projected climate. I used the LANDIS-II model to simulate carbon dynamics under early (2010-2019), mid (2050-2059), and late (2090-2099) century climate projections for a ponderosa pine (Pinus ponderosa) dominated landscape in northern Arizona. I ran 100-year simulations with two different treatments (control, thin and burn) and a 1 in 50 chance of wildfire occurring. I found that control TEC had a consistent decline throughout the simulation period, regardless of climate. Thin and burn TEC increased following treatment implementation and showed more differentiation than the control in response to climate, with late-century climate having the lowest TEC. Treatment efficacy, as measured by mean fire severity, was not impacted by climate. Fire effects were evident in the cumulative net ecosystem exchange (NEE) for the different treatments. Over the simulation period, 32.8-48.9% of the control landscape was either C neutral or a C source to the atmosphere and greater than 90% of the thin and burn landscape was a moderate C sink. These results suggest that in southwestern ponderosa pine, restoring forest structure and surface fire regimes provides a reasonable hedge against the uncertainty of future climate change for maintaining the forest C sink.

Ecosystem carbon carrying capacity (CCC) is determined by prevailing climate and natural disturbance regimes, conditions that are projected to change significantly. The interaction of changing climate and its effects on disturbance regimes is expected to affect forest regeneration and growth, which may diminish forest carbon (C) stocks and uptake. We modeled landscape C dynamics over 590 years along the latitudinal gradient of the U.S. Sierra Nevada Mountains under climate and area burned by large wildfires projected by late 21 st century. We assumed climate and wildfire stabilize at late-21 st century conditions (2090–2100) to facilitate analysis of lags between warming and changing CCC. We show that compared with historical (1980–2010) climate and wildfire conditions, projected scenarios would drive a significant decrease of up to 73% in mean total ecosystem carbon (TEC) by the end of the 590-year simulation. Tree regeneration failure due to intensified growing season dryness and increased area burned would substantially decrease forested area, transitioning the system from C sink to source. Our results demonstrate the potential for a lower CCC in the system due to extensive vegetation type conversion from forest to non-forest types, and suggest a decline in the contribution of Sierra Nevada forests to U.S. C sink.

Historically, fire has been essential in Southwestern US forests. However, a century of fire-exclusion and changing climate created forests which are more susceptible to uncharacteristically severe wildfires. Forest managers use a combination of thinning and prescribed burning to reduce forest density to help mitigate the risk of high-severity fires. These treatments are laborious and expensive, therefore optimizing their impact is crucial. Landscape simulation models can be useful in identifying high risk areas and assessing treatment effects, but uncertainties in these models can limit their utility in decision making. In this study we examined underlying uncertainties in the initial vegetation layer by leveraging a previous study from the Santa Fe fireshed and using new inventory plots from 111 stands to interpolate the initial forest conditions. We found that more inventory plots resulted in a different geographic distribution and wider range of the modelled biomass. This changed the location of areas with high probability of high-severity fires, shifting the optimal location for management. The increased range of biomass variability from using a larger number of plots to interpolate the initial vegetation layer also influenced ecosystem carbon dynamics, resulting in simulated forest conditions that had higher rates of carbon uptake. We conclude that the initial forest layer significantly affects fire and carbon dynamics and is dependent on both number of plots, and sufficient representation of the range of forest types and biomass density.

Background In the southwestern United States, post-fire vegetation recovery is increasingly variable in forest burned at high severity. Many factors, including temperature, drought, and erosion, can reduce post-fire vegetation recovery rates. Here, we examined how year-of-fire precipitation variability, topography, and soils influenced post-fire vegetation recovery in the southwestern United States as measured by greenness to determine whether erosion-related factors would have persistent effects in the longer post-fire period. We modeled relationships between post-fire vegetation and these predictors using random forest and examined changes in post-fire normalized burn ratio across fires in Arizona and New Mexico. We incorporated growing season climate to determine if year-of-fire effects were persistent during the subsequent 5 years or if temperature, water deficit, and precipitation in the years following fire were more influential for vegetation greenness. We expected that post-fire factors that drive erosion would reduce greenness; however, these effects would explain less variability in post-fire greenness than growing season climate. Results We found reductions in post-fire greenness in areas burned at high severity when heavy and intense precipitation fell on more erodible soils immediately post-fire. In highly erodible scenarios, when accounting for growing season climate, coefficient of variation for year-of-fire precipitation, total precipitation, and soil erodibility decreased greenness in the fifth year. However, more of the variation in greenness was explained by variability of growing season vapor pressure deficit and growing season precipitation. Conclusions Our results suggest that while the factors that contribute to post-fire erosion and its effects on vegetation recovery are important, at a regional scale, the majority of the variability in post-fire greenness in high-severity burned areas in southwestern forests is due to climatic drivers such as growing season precipitation and vapor pressure deficit. Given the increasing scale of area burned at high severity and the potential for more post-fire erosion, quantifying how these factors alter ecosystem development is central to understanding how different ecosystem types will be distributed across these landscapes with additional climate change.

Management of forests for carbon uptake is an important tool in the effort to slow the increase in atmospheric CO₂ and global warming. However, some current policies governing forest carbon credits actually promote avoidable CO₂ release and punish actions that would increase long‐term carbon storage. In fire‐prone forests, management that reduces the risk of catastrophic carbon release resulting from stand‐replacing wild‐fire is considered to be a CO₂ source, according to current accounting practices, even though such management may actually increase long‐term carbon storage. Examining four of the largest wildfires in the US in 2002, we found that, for forest land that experienced catastrophic stand‐replacing fire, prior thinning would have reduced CO₂ release from live tree biomass by as much as 98%. Altering carbon accounting practices for forests that have historically experienced frequent, low‐severity fire could provide an incentive for forest managers to reduce the risk of catastrophic fire and associated large carbon release events.

Large-scale global reforestation goals have been proposed to help mitigate climate change and provide other ecosystem services. To explore reforestation potential in the United States, we used GIS analyses, surveys of nursery managers and foresters, and literature synthesis to assess the opportunities and challenges associated with meeting proposed reforestation goals. We considered a scenario where 26 million hectares (64 million acres) of natural and agricultural lands are reforested by 2040 with 30 billion trees at an estimated cost of $33 ($24–$53) billion USD. Cost per hectare will vary by region, site conditions, and other factors. This scenario would require increasing the number of tree seedlings produced each year by 1.7 billion, a 2.3-fold increase over current nursery production levels. Additional investment (not included in the reforestation cost estimate) will be needed to expand capacity for seed collection, seedling production, workforce development, and improvements in pre- and post-planting practices. Achieving this scenario will require public support for investing in these activities and incentives for landowners.

Wildfire severity is a key indicator of both direct ecosystem impacts and indirect emissions impacts that affect air quality, climate, and public health far beyond the spatial footprint of the flames. Comprehensive, accurate inventories of severity and emissions are essential for assessing these impacts and setting appropriate fire management and health care preparedness strategies, as is the ability to project emissions for future wildfires. The frequency of large wildfires and the magnitude of their impacts have increased in recent decades, fueling concerns about decreased air quality. To improve the availability of accurate fire severity and emissions estimates, we developed the wildfire burn severity and emissions inventory (WBSE). WBSE is a retrospective spatial burn severity and emissions inventory at 30 m resolution for event-based assessment and 500 m resolution for daily emissions calculation. We applied the WBSE framework to calculate burn severity and emissions for historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California, U.S., a substantially more extended period than existing inventories. We assigned the day of burning and daily emissions for each fire during 2002–2020. The framework described here can also be applied to estimate severity for smaller wildfires and can also be used to estimate emissions for fires simulated in California for future climate and land-use scenarios. The WBSE framework implemented in R and Google Earth Engine can provide quick estimates once a desired fire perimeter is available. The framework developed here could also easily be applied to other regions with user-modified vegetation, fuel data, and emission factors.

Management of forest lands under climate warming poses challenges to managers, some of which are difficult to predict. Examining the trade‐offs associated with forest stewardship choices is essential to avoid consequences associated with loss of natural capital. We utilized LANDIS‐II process model simulations for three forested sites located in disparate parts of the United States with the purpose of understanding the trade‐offs imposed by management choices under climate warming and associated wildfire. There were only small trade‐offs that emerged from the simulations, between habitat area for threatened and endangered species (TES), net ecosystem exchange for CO2 (NEE), and risk of wildfire. Stand management in the form of thinning and prescribed burning typically increased NEE while simultaneously increasing habitat for TES, while reducing the risk of wildfire. These benefits were also observed under a climate warming scenario; however, the benefits were greatly outweighed by the negative impacts of warming on both TES habitat and NEE. Balancing these ecosystem services via thinning and burning treatments is a strategic approach to mitigate risks of wildfire both currently and under a warming future climate.

Climate warming in the western United States is causing changes to the wildfire regime in mixed‐conifer forests. Rising temperatures, longer fire seasons, increased drought, as well as fire suppression and changes in land use, have led to greater and more severe wildfire activity, all contributing to altered forest composition over the past century. To understand future interactions among climate, wildfire, and vegetation in a fire‐prone landscape in the southern Blue Mountains of central Oregon, we used a spatially explicit forest landscape model, LANDIS‐II, to simulate forest and fire dynamics under current management practices and two projected climate scenarios. The results suggest that wildfires will become more frequent, more extensive, and more severe under projected climate than contemporary climate. Furthermore, projected climate change generated a 20% increase in the number of extreme fire years (years with at least 40,000 ha burned). This caused large shifts in tree species composition, characterized by a decline in the sub‐alpine species (Abies lasiocarpa, Picea engelmannii, Pinus albicaulis) and increases in lower‐elevation species (Pinus ponderosa, Abies grandis), resulting in forest homogenization across the elevational gradient. This modeling study suggests that climate‐driven increases in fire activity and severity will make high‐elevation species vulnerable to decline and will reduce landscape heterogeneity. These results underscore the need for forest managers to actively consider climate change, altered fire regimes, and projected declines in sub‐alpine species in their long‐term management plans.

Climate change is altering wildfire and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size. The ability to predict future burn severity patterns could better support policy and land management decisions. Here we demonstrate a methodology to first, statistically estimate individual burn severity classes at 30 meters and second, cluster and smooth high severity patches onto a known landscape. Our goal here was not to exactly replicate observed burn severity maps, but rather to utilize observed maps as one realization of a random process dependent on climate, topography, fire weather, and fuels, to inform creation of additional realizations through our simulation technique. We developed two sets of empirical models with two different vegetation datasets to test if coarse vegetation could accurately model for burn severity. While visual acuity can be used to assess the performance of our simulation process, we also employ the Ripley’s K function to compare spatial point processes at different scales to test if the simulation is capturing an appropriate amount of clustering. We utilize FRAGSTATS to obtain high severity patch metrics to test the contiguity of our high severity simulation. Ripley’s K function helped identify the number of clustering iterations and FRAGSTATS showed how different focal window sizes affected our ability to cluster high severity patches. Improving our ability to simulate burn severity may help advance our understanding of the potential influence of land and fuels management on ecosystem-level response variables that are important for decision-makers. Simulated burn severity maps could support managing habitat and estimating risks of habitat loss, protecting infrastructure and homes, improving future wildfire emissions projections, and better mapping and planning for fuels treatment scenarios.