Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk.

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.

CO2 exchange and evapotranspiration across dryland ecosystems of southwestern North America.

Global-scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink. However, such analyses are poorly constrained by measured CO2 exchange in drylands. Here we address this observation gap with eddy covariance data from 25 sites in the water-limited Southwest region of North America with observed ranges in annual precipitation of 100-1000 mm, annual temperatures of 2-25°C, and records of 3-10 years (150 site-years in total). Annual fluxes were integrated using site-specific ecohydrologic years to group precipitation with resulting ecosystem exchanges. We found a wide range of carbon sink/source function, with mean annual net ecosystem production (NEP) varying from -350 to +330 gCm-2 across sites with diverse vegetation types, contrasting with the more constant sink typically measured in mesic ecosystems. In this region, only forest-dominated sites were consistent carbon sinks. Interannual variability of NEP, gross ecosystem production (GEP), and ecosystem respiration (Reco ) was larger than for mesic regions, and half the sites switched between functioning as C sinks/C sources in wet/dry years. The sites demonstrated coherent responses of GEP and NEP to anomalies in annual evapotranspiration (ET), used here as a proxy for annually available water after hydrologic losses. Notably, GEP and Reco were negatively related to temperature, both interannually within site and spatially across sites, in contrast to positive temperature effects commonly reported for mesic ecosystems. Models based on MODIS satellite observations matched the cross-site spatial pattern in mean annual GEP but consistently underestimated mean annual ET by ~50%. Importantly, the MODIS-based models captured only 20-30% of interannual variation magnitude. These results suggest the contribution of this dryland region to variability of regional to global CO2 exchange may be up to 3-5 times larger than current estimates.

Global satellite monitoring of climate-induced vegetation disturbances.

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.