Geology topics

Xiaohui Feng

Publications and source records attributed to Xiaohui Feng.

2 recordsLinked to original sources

USGS2020

Forest management under megadrought: Urgent actions needed at finer-scale and higher intensity

Drought and warming increasingly are causing widespread tree die-offs and extreme wildfires. Forest managers are struggling to improve anticipatory forest management practices given more frequent, extensive, and severe wildfire and tree die-off events triggered by “hotter drought”—drought under warmer than historical conditions. Of even greater concern is the increasing probability of multi-year droughts, or “megadroughts”—persistent droughts that span years to decades, and that under a still-warming climate, will also be hotter than historical norms. Megadroughts under warmer temperatures are disconcerting because of their potential to trigger more severe forest die-off, fire cycles, pathogens, and insect outbreaks. In this Perspective, we identify potential anticipatory and/or concurrent options for non-timber forest management actions under megadrought, which by necessity are focused more at finer spatial scales such as the stand level using higher-intensity management. These management actions build on silvicultural practices focused on growth and yield (but not harvest). Current management options that can be focused at finer scales include key silvicultural practices: selective thinning; use of carefully selected forward-thinking seed mixes; site contouring; vegetation and pest management; soil erosion control; and fire management. For the extreme challenges posed by megadroughts, management will necessarily focus even more on finer-scale, higher-intensity actions for priority locations such as fostering stand refugia; assisted stand recovery via soil amendments; enhanced root development; deep soil water retention; and shallow water impoundments. Drought-induced forest die-off from megadrought likely will lead to fundamental changes in the structure, function, and composition of forest stands and the ecosystem services they provide.

Frontiers in Forests and Global Change↗

USGS2017

Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling

Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured inter-annual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including above-ground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.

Global Change Biology↗