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Nagy, R. C.

Publications and source records attributed to Nagy, R. C..

2 recordsLinked to original sources

Postfire recovery of western US conifer forests (1984-2017) using space-borne lidar data

Coniferous forests account for 78% of the western US forests and store a substantial amount of carbon. Wildfires significantly alter vegetation structure, and hence the forest carbon stock. This study evaluates post-fire vegetation recovery trajectories and rates across the western US using recently launched Global Ecosystem Dynamic Investigations (GEDI) mission lidar data. Three ecoregions studied here, the Pacific Northwest, Southern Rockies, and Northern Rockies, show fire severity and ecoregion specific recovery trajectories for canopy height (CH), plant area index (PAI), and the foliage height diversity (FHD). The recovery trajectories are characterized by an initial decline in vegetation structure (CH, PAI, and FHD) during the first 9-25 years postfire followed by a gain of the structure. Regions of low burn severity can fully recover to the unburned background state within the first three decades while the high burn severity regions may recover in the first century, but only in the absence of fires within this period. The PNW exhibits the slowest recovery rate. According to our results, all three ecoregions feature a loss of growing stock volume (GSV) (-1% - - 48%). Time since fire, fire severity, and altitude were identified as the most significant drivers of postfire vegetation recovery, likely because they integrate the distance to seed source, vegetation composition, and the local climate. Our study suggests that, if fire return intervals become shorter than 50 years, these three ecoregions will have significantly reduced their woody vegetation, hence the carbon stocks. In addition, all the ecoregions studied here exhibit extensive impacts from other disturbances such as beetle invasion. It is therefore important to consider the effects of compound disturbances on vegetation recovery trajectories to infer the future carbon potential in these ecosystems.

ecology↗

Understanding and predicting extreme wildfires in the contiguous United States

Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30 year wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.

ecology↗