Restoration of forest resilience to fire from old trees is possible across a large Colorado dry-forest landscape by 2060, but only under the Paris 1.5℃ goal

Restoration treatments enhance tree growth and alter climatic constraints during extreme drought

The frequency and severity of drought events are predicted to increase due to anthropogenic climate change, with cascading effects across forested ecosystems. Management activities such as forest thinning and prescribed burning, which are often intended to mitigate fire hazard and restore ecosystem processes, may also help promote tree resistance to drought. However, it is unclear whether these treatments remain effective during the most severe drought conditions or whether their impacts differ across environmental gradients. We used tree-ring data from a system of replicated, long-term (>20 years) experiments in the southwestern United States to evaluate the effects of forest restoration treatments (i.e., evidence-based thinning and burning) on annual growth rates (i.e., basal area increment; BAI) of ponderosa pine (Pinus ponderosa), a broadly distributed and heavily managed species in western North America. The study sites were established at the onset of the most extreme drought event in at least 1200 years and span much of the climatic niche of Rocky Mountain ponderosa pine. Across sites, tree-level BAI increased due to treatment, where trees in treated units grew 133.1% faster than trees in paired, untreated units. Likewise, trees in treated units grew an average of 85.6% faster than their pre-treatment baseline levels (1985 to ca. 2000), despite warm, dry conditions in the post-treatment period (ca. 2000-2018). Variation in the local competitive environment promoted variation in BAI, and larger trees were the fastest-growing individuals, irrespective of treatment. Tree thinning and prescribed fire altered the climatic constraints on growth, decreasing the effects of belowground moisture availability and increasing the effects of atmospheric evaporative demand over multi-year timescales. Our results illustrate that restoration treatments can enhance tree-level growth across sites spanning ponderosa pine's climatic niche, even during recent, extreme drought events. However, shifting climatic constraints, combined with predicted increases in evaporative demand in the southwestern United States, suggest that the beneficial effects of such treatments on tree growth may wane over the upcoming decades.

AI and climate resilience governance

While artificial intelligence (AI) offers promising solutions to address climate change impacts, it also raises many application limitations and challenges. A risk governance perspective is used to analyze the role of AI in supporting decision-making for climate adaptation, spanning risk assessment, policy analysis, and implementation. This comprehensive review combines expert insights and systematic literature review. The study's findings indicate a large emphasis on applying AI to climate "risk assessments," particularly regarding hazard and exposure assessment, but a lack of innovative approaches and tools to evaluate resilience and vulnerability as well as prioritization and implementation process, all of which involve subjective, qualitative, and context-specific elements. Additionally, the study points out challenges such as difficulty of simulating complex long-term changes, and evolving policies and human behavior, reliance on data quality and computational resources, and the need for improved interpretability of results as areas requiring further development.

Effects of forest management practices on carbon dynamics of China's boreal forests under changing climates

Climate change and forest management practices influence forest productivity and carbon budgets, and understanding their interactions is necessary to develop accurate predictions of carbon dynamics as many countries in the world strive towards carbon neutrality. Here, we developed a model-coupling framework to simulate the carbon dynamics of boreal forests in China. The expected dynamics of forest recovery and change following intense timber harvesting in the recent past and projected carbon dynamics into the future under different climate change scenarios and forest management practices (e.g., restoration, afforestation, tending, and fuel management). We predict that under current management strategies, climate change would lead to increased fire frequency and intensity, eventually shifting these forests from carbon sinks towards being carbon sources. This study suggests that future boreal forest management should be altered to reduce the probability of fire occurrence and carbon losses caused by catastrophic fires through planting deciduous species, mechanical removal, and prescribed fire.

A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support

Wildfires threaten and kill people, destroy urban and rural property, degrade air quality, ravage forest ecosystems, and contribute to global warming. Wildfire management decision support models are thus important for avoiding or mitigating the effects of these events. In this context, this paper aims at providing a review of recent applications of machine learning methods for wildfire management decision support. The emphasis is on providing a summary of these applications with a classification according to the case study type, machine learning method, case study location, and performance metrics. The review considers documents published in the last four years, using a sample of 135 documents (review articles and research articles). It is concluded that the adoption of machine learning methods may contribute to enhancing support in different fire management phases.