A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.

With the recently observed and projected trends of growing wildland fire occurrence in high northern latitudes, satellite-based burned area mapping in these regions is becoming increasingly important for scientific and fire management communities. Coarse- and moderate-resolution remotely sensed data products are the only viable source of comprehensive and timely estimates of burned area in remote, sparsely populated regions. Several MODIS (Moderate Resolution Imaging Spectroradiometer)-based burned area products for Alaska are currently available. However, our research shows that the existing burned area products underestimate the extent of the effect of fire by 15–70%. Environmental conditions limit the effective observation of land surface in Alaska to the period between May and September. These limitations are particularly noticeable in mapping late-season fires. Here we present an ecosystem-based modification to a previously developed burned area mapping approach designed to enhance the algorithm performance in Alaska. The mapping results show a consistently high performance of the adjusted algorithm in mapping burned areas in Alaska during large (2004 and 2005) and small (2006 and 2007) fire years. The adjusted burned area product maps burned areas identified by the Monitoring Trends in Burn Severity products with the overall accuracy of 90–93% and Kappa of 0.67–0.75%.

Over the last several decades, warming in the Arctic has outpaced the already impressive increases in global mean temperatures. The impact of these increases in temperature has been observed in a multitude of ecological changes in North American tundra including changes in vegetative cover, depth of active layer, and surface water extent. The low topographic relief and continuous permafrost create an ideal environment for the formation of small water bodies—a definitive feature of tundra surface. In this study, water bodies in Nunavut territory in northern Canada were mapped using a long-term record of remotely sensed observations at 30 m spatial resolution from the Landsat suite of instruments. The temporal trajectories of water extent between 1985 and 2015 were assessed. Over 675,000 water bodies have been identified over the 31-year study period with over 168,000 showing a significant (p < 0.05) trend in surface area. Approximately 55% of water bodies with a significant trend were increasing in size while the remaining 45% were decreasing in size. The overall net trend for water bodies with a significant trend is 0.009 ha year⁻¹ per water body.

The Siberian larch forests, taking up about a fifth of the global boreal biome, are different from the North American boreal forests in that they generally do not undergo a secondary succession. While wildfires in the boreal forests in North America have been shown to exert a cooling effect on the climate system through a sharp increase in surface albedo associated with canopy removal and species composition change during succession, the magnitude of the surface forcing resulting from fire-induced albedo change and its longevity in Siberia have not been previously quantified. Here we show that in contrast to previous expectations, stand-replacing fires exert a strong cooling effect similar in magnitude to that in North America. This cooling effect is attributable to the increase in surface albedo during snow-on periods. However, the observed earlier snowmelt in the region, and subsequently a longer snow-free season, has resulted in a warming effect which has the potential to offset the fire-induced cooling. The net albedo-induced forcing of the Siberian larch forests in the future would hinge on the interaction between the fire-induced cooling effect and the climate-induced warming effect, both of which will be impacted by the expected further warming in the region.

Wildfire is a dominant disturbance agent in pan‐Arctic tundra and can significantly impact terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrence remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud‐to‐ground (CG) lightning to be the dominant driver controlling tundra fire occurrence. Near‐surface weather warmer and drier than normal was required to support burning, while fuel composition and topography have modest correlations with fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning in modeling is essential for fire monitoring, forecasting, and management in the Arctic.