Zachary A. Holden

Climate-induced variations in global wildfire danger from 1979 to 2013

Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earths vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.

Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006

Fire is a keystone process in many ecosystems of western North America. Severe fires kill and consume large amounts of above- and belowground biomass and affect soils, resulting in long-lasting consequences for vegetation, aquatic ecosystem productivity and diversity, and other ecosystem properties. We analyzed the occurrence of, and trends in, satellite-derived burn severity across six ecoregions in the Southwest and Northwest regions of the United States from 1984 to 2006 using data from the Monitoring Trends in Burn Severity project. Using 1,024 fires from the Northwest (4,311,871 ha) and 497 fires from the Southwest (1,434,670 ha), we examined the relative influence of fine-scale topography and coarse-scale weather and climate on burn severity (the degree of change from before the fire to one year after) using the Random Forest machine learning algorithm. Together, topography, climate, and weather explained severe fire occurrence with classification accuracies ranging from 68% to 84%. Topographic variables were relatively more important predictors of severe fire occurrence than either climate or weather variables. Predictability of severe fire was consistently lower during years with widespread fires, suggesting that local control exerted by topography may be overwhelmed by regional climatic controls when fires burn in dry conditions. Annually, area burned severely was strongly correlated with area burned in all ecoregions (Pearsons correlation 0.86–0.97; p < 0.001), while the proportion of area burned severely was significantly correlated with area burned only in two ecoregions (p ≤ 0.037). During our short time series, only ecoregions in the Southwest showed evidence of a significant increase (p ≤ 0.036) in annual area burned and area burned severely, and annual proportion burned severely increased in just one of the three Southwest ecoregions. We suggest that predictive mapping of the potential for severe fire is possible, and will be improved with climate data at the scale of the topographic and Landsat-derived burn severity data. Although severity is a value-laden term implying negative ecosystem effects, we stress that severity can be objectively measured and recognize that high severity fire is an important ecological process within the historical range of variability in some ecosystems.

Climate refugia: joint inference from fossil records, species distribution models and phylogeography

Climate refugia, locations where taxa survive periods of regionally adverse climate, are thought to be critical for maintaining biodiversity through the glacial-interglacial climate changes of the Quaternary. A critical research need is to better integrate and reconcile the three major lines of evidence used to infer the existence of past refugia - fossil records, species distribution models and phylogeographic surveys - in order to characterize the complex spatiotemporal trajectories of species and populations in and out of refugia. Here we review the complementary strengths, limitations and new advances for these three approaches. We provide case studies to illustrate their combined application, and point the way towards new opportunities for synthesizing these disparate lines of evidence. Case studies with European beech, Qinghai spruce and Douglas-fir illustrate how the combination of these three approaches successfully resolves complex species histories not attainable from any one approach. Promising new statistical techniques can capitalize on the strengths of each method and provide a robust quantitative reconstruction of species history. Studying past refugia can help identify contemporary refugia and clarify their conservation significance, in particular by elucidating the fine-scale processes and the particular geographic locations that buffer species against rapidly changing climate.

The climate velocity of the contiguous United States during the 20th century

Rapid climate change has the potential to affect economic, social, and biological systems. A concern for species conservation is whether or not the rate of on-going climate change will exceed the rate at which species can adapt or move to suitable environments. Here we assess the climate velocity (both climate displacement rate and direction) for minimum temperature, actual evapotranspiration, and climatic water deficit (deficit) over the contiguous US during the 20th century (1916-2005). Vectors for these variables demonstrate a complex mosaic of patterns that vary spatially and temporally and are dependent on the spatial resolution of input climate data. Velocities for variables that characterize the climatic water balance were similar in magnitude to that derived from temperature, but frequently differed in direction resulting in the divergence of climate vectors through time. Our results strain expectations of poleward and upslope migration over the past century due to warming. Instead, they suggest that a more full understanding of changes in multiple climatic factors, in addition to temperature, may help explain unexpected or conflicting observational evidence of climate-driven species range shifts during the 20th century.

The missing mountain water: Slower westerlies decrease orographic enhancement in the Pacific Northwest USA

Up and Down Climate change is expected to have significant impacts on Earths hydrological cycle, owing to changes, for example, in air temperature, weather patterns, and land surface evaporation. In the Pacific Northwest of the United States, decreases in stream flow have been attributed to increased temperatures. Luce et al. (p. 1360, published online 29 November) show that these changes in stream flow are not likely to have been caused by temperature change, however, but rather by decreasing precipitation in the mountains where the streams originate. Weakening westerly winds since 1950 have decreased streamflows across the Pacific Northwest United States. Trends in streamflow timing and volume in the Pacific Northwest United States have been attributed to increased temperatures, because trends in precipitation at lower-elevation stations were negligible. We demonstrate that observed streamflow declines are probably associated with declines in mountain precipitation, revealing previously unexplored differential trends. Lower-troposphere winter (November to March) westerlies are strongly correlated with high-elevation precipitation but weakly correlated with low-elevation precipitation. Decreases in lower-tropospheric winter westerlies across the region from 1950 to 2012 are hypothesized to have reduced orographic precipitation enhancement, yielding differential trends in precipitation across elevations and contributing to the decline in annual streamflow. Climate projections show weakened lower-troposphere zonal flow across the region under enhanced greenhouse forcing, highlighting an additional stressor that is relevant for climate change impacts on hydrology.

Challenges of assessing fire and burn severity using field measures, remote sensing and modelling

Comprehensive assessment of ecological change after fires have burned forests and rangelands is important if we are to understand, predict and measure fire effects. We highlight the challenges in effective assessment of fire and burn severity in the field and using both remote sensing and simulation models. We draw on diverse recent research for guidance on assessing fire effects on vegetation and soil using field methods, remote sensing and models. We suggest that instead of collapsing many diverse, complex and interacting fire effects into a single severity index, the effects of fire should be directly measured and then integrated into severity index keys specifically designed for objective severity assessment. Using soil burn severity measures as examples, we highlight best practices for selecting imagery, designing an index, determining timing and deciding what to measure, emphasising continuous variables measureable in the field and from remote sensing. We also urge the development of a severity field assessment database and research to further our understanding of causal mechanisms linking fire and burn severity to conditions before and during fires to support improved models linking fire behaviour and severity and for forecasting effects of future fires.

Review of fuel treatment effectiveness in forests and rangelands and a case study from the 2007 megafires in central, Idaho, USA

This report provides managers with the current state of knowledge regarding the effectiveness of fuel treatments for mitigating severe wildfire effects. A literature review examines the effectiveness of fuel treatments that had been previously applied and were subsequently burned through by wildfire in forests and rangelands. A case study focuses on WUI fuel treatments that were burned in the 2007 East Zone and Cascade megafires in central Idaho. Both the literature review and case study results support a manager consensus that forest thinning followed by some form of slash removal is most effective for reducing subsequent wildfire severity.

Sensitivity of snowpack storage to precipitation and temperature using spatial and temporal analog models

Empirical sensitivity analyses are important for evaluation of the effects of a changing climate on water resources and ecosystems. Although mechanistic models are commonly applied for evaluation of climate effects for snowmelt, empirical relationships provide a first-order validation of the various postulates required for their implementation. Previous studies of empirical sensitivity for April 1 snow water equivalent (SWE) in the western United States were developed by regressing interannual variations in SWE to winter precipitation and temperature. This offers a temporal analog for climate change, positing that a warmer future looks like warmer years. Spatial analogs are used to hypothesize that a warmer future may look like warmer places, and are frequently applied alternatives for complex processes, or states/metrics that show little interannual variability (e.g., forest cover). We contrast spatial and temporal analogs for sensitivity of April 1 SWE and the mean residence time of snow (SRT) using data from 524 Snowpack Telemetry (SNOTEL) stations across the western U.S. We built relatively strong models using spatial analogs to relate temperature and precipitation climatology to snowpack climatology (April 1 SWE, R2=0.87, and SRT, R2=0.81). Although the poorest temporal analog relationships were in areas showing the highest sensitivity to warming, spatial analog models showed consistent performance throughout the range of temperature and precipitation. Generally, slopes from the spatial relationships showed greater thermal sensitivity than the temporal analogs, and high elevation stations showed greater vulnerability using a spatial analog than shown in previous modeling and sensitivity studies. The spatial analog models provide a simple perspective to evaluate potential futures and may be useful in further evaluation of snowpack with warming.

Tracking Interannual Streamflow Variability with Drought Indices in the U.S. Pacific Northwest

AbstractDrought indices are often used for monitoring interannual variability in macroscale hydrology. However, the diversity of drought indices raises several issues: 1) which indices perform best and where; 2) does the incorporation of potential evapotranspiration (PET) in indices strengthen relationships, and how sensitive is the choice of PET methods to such results; 3) what additional value is added by using higher-spatial-resolution gridded climate layers; and 4) how have observed relationships changed through time. Standardized precipitation index, standardized precipitation evapotranspiration index (SPEI), Palmer drought severity index, and water balance runoff (WBR) model output were correlated to water-year runoff for 21 unregulated drainage basins in the Pacific Northwest of the United States. SPEI and WBR with time scales encompassing the primary precipitation season maximized the explained variance in water-year runoff in most basins. Slightly stronger correlations were found using PET estima...

Remotely Sensed Land Skin Temperature as a Spatial Predictor of Air Temperature across the Conterminous United States

AbstractRemotely sensed land skin temperature (LST) is increasingly being used to improve gridded interpolations of near-surface air temperature. The appeal of LST as a spatial predictor of air temperature rests in the fact that it is an observation available at spatial resolutions fine enough to capture topoclimatic and biophysical variations. However, it remains unclear if LST improves air temperature interpolations over what can already be obtained with simpler terrain-based predictor variables. Here, the relationship between LST and air temperature is evaluated across the conterminous United States (CONUS). It is found that there are significant differences in the ability of daytime and nighttime observations of LST to improve air temperature interpolations. Daytime LST mainly indicates finescale biophysical variation and is generally a poorer predictor of maximum air temperature than simple linear models based on elevation, longitude, and latitude. Moderate improvements to maximum air temperature int...